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Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team Growth Platform builds the machine learning systems that help businesses discover and use the Stripe products that meet their needs. Our recommendations reach users across the Dashboard, email, onboarding, documentation, and AI agent interfaces. We combine an understanding of each business with models that decide which recommendation is useful, when to show it, and how to learn from the outcome. Our work spans recommendation and ranking models, contextual bandits, agent-based recommendations, and the data and evaluation systems behind them. We build shared capabilities that product, marketing, and sales teams can use across Stripe. Success means helping businesses take useful actions and adopt products that help them grow, while keeping recommendations relevant and avoiding unnecessary messages. What you’ll do You will build and operate production ML systems that improve how Stripe recommends products, content, and next steps to businesses. You will own work from problem definition and feature development through training, evaluation, deployment, monitoring, and iteration. Working with data scientists, engineers, and product partners, you will turn model improvements into measurable user and business outcomes. Responsibilities Design, train, evaluate, deploy, and maintain models for recommendation, ranking, and personalized action selection across Growth Platform surfaces. Improve contextual bandit and policy-learning approaches, including exploration, reward design, and how recommendations adapt to user context and feedback. Build agent-based recommendation capabilities that use business context to identify relevant products and integration options, with evaluations that test recommendation quality and usefulness. Develop reliable data and feature pipelines for training and inference. Improve data freshness, feature quality, and consistency between training and production. Build reusable tooling for model evaluation, retraining, and safe rollout so the team can test and ship improvements faster. Own the quality and operation of the team's ML components: write tested production code, monitor models and pipelines, investigate failures, and improve reliability, latency, and cost. Design and analyze online experiments with data science partners. Connect offline evaluation to product adoption and incremental impact, with guardrails for dismissals, unsubscribes, and user experience. Partner with product engineering to integrate models into recommendation delivery systems, and with ML infrastructure teams to use and improve Stripe's shared training, feature, and serving capabilities. Work with product, marketing, and sales partners to identify problems that shared ML capabilities can solve, and make practical choices about where modeling adds value. Who you are You are a machine learning engineer with a builder mindset. You care about the business problem, the quality of the model, and what happens after it ships. You can move between modeling and software engineering, make practical tradeoffs, and take ownership of an ambiguous problem through production and measurement. We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 3+ years of industry experience in machine learning engineering, software engineering, or applied data science, with hands-on experience building and shipping ML models in production. Strong programming skills in Python and experience writing maintainable, tested production code. Practical experience designing, training, and evaluating ML models using frameworks such as PyTorch, TensorFlow, XGBoost, or scikit-learn. Experience building data or feature pipelines, proficiency in SQL, and familiarity with distributed data processing tools such as Spark or PySpark. A strong understanding of statistics, model evaluation, and experimentation, including the ability to recognize data leakage and distinguish offline model improvements from business impact. Experience deploying, monitoring, and debugging production ML systems, and evaluating tradeoffs among model quality, reliability, latency, and cost. Ability to turn an open-ended business problem into a technical approach and collaborate effectively with engineering, data science, product, and business partners. Preferred qualifications Experience with recommendation systems, ranking, personalization, or marketplace and advertising optimization. Experience with contextual bandits, policy learning, causal inference, or off-policy evaluation. Experience building and evaluating LLM applications, including structured extraction, embeddings, or recommendations grounded in user and business context. Experience building reusable ML capabilities used by multiple products or teams, including training automation, feature systems, or model monitoring. Experience with product growth, lifecycle messaging, or systems that balance short-term engagement with longer-term user outcomes.
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team Link is a digital wallet designed for fast and secure online payments. It allows consumers to save and use their preferred payment methods across the Link network, helping them check out quickly and securely wherever Link is accepted. The Link Fraud and Auth team works to make Link the most trusted and highest-performing way to pay. We protect consumers and merchants from fraud, abuse, and financial loss while maximizing authorization rates for good users. Our work spans consumer-facing experiences, payment infrastructure, and ML powered risk systems. We manage fraud and financial risk across a growing range of novel Link features, including Link’s agentic wallet, stored balance, and LPMs. The team also owns Instant Bank Payments, a proprietary payment method built on ACH rails, offering merchants immediate confirmation while protecting them from bank-initiated returns. IBP is the heart of Link’s revenue engine, giving LFA engineers the opportunity to shape and scale one of Link’s most important products. What you’ll do As a machine learning engineer on Link Fraud and Auth, you’ll build and operate models and risk decisioning systems that protect Link while helping more legitimate payments succeed. You’ll work across the full machine learning lifecycle, from analyzing fraud patterns and identifying opportunities to building, deploying, monitoring, and improving models in production. You’ll use data to form hypotheses, make practical modeling choices, and define technical direction in partnership with Engineering, Product, and Data Science. Your work will directly influence Link’s fraud performance, authorization rates, and ability to expand into new products and payment experiences. Responsibilities Build, train, evaluate, deploy, and own machine learning models that detect fraud and abuse across Link. Use large-scale datasets to investigate emerging threats, develop hypotheses, and identify opportunities to improve payment performance. Develop pragmatic machine learning solutions, including tree-based models and other approaches suited to real-time risk decisioning. Design data pipelines, features, evaluation methods, experiments, and monitoring systems that support reliable production models. Build and improve risk decisioning systems that integrate with other parts of Stripe’s payments stack. Own ambiguous problems from initial analysis and problem definition through technical design, implementation, launch, measurement, and iteration. Collaborate with Engineering, Product, Data Science, and Risk partners across Stripe to turn model improvements into durable product outcomes. Who you are We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 6+ years of industry experience building and shipping machine learning models in production. Strong programming skills in Python and experience with common data and machine learning tools, such as SQL, Spark, and XGBoost. Strong knowledge of production machine learning systems, including data pipelines, feature development, model evaluation, deployment, monitoring, and iteration. Experience working with large and complex datasets and applying data analysis, statistics, and experimentation fundamentals. Demonstrated ability to take an open-ended business problem, determine where machine learning can help, and own the solution through production. Strong judgment in selecting practical modeling approaches and evaluating tradeoffs among model performance, system complexity, latency, and business impact. Strong collaboration skills and the ability to work across teams and contribute to peers' success. Preferred qualifications Experience applying machine learning to fraud detection, risk modeling, payment authorization, identity, account security, or another adversarial domain. Experience building real-time, low-latency machine learning or risk decisioning systems at scale. Experience integrating models into production services and designing reliable systems around model outputs. Experience with payments, fintech, digital wallets, or money movement. Strong software engineering skills and experience designing solutions across the machine learning and product stack.
Who we are About the team Financial Connections is Stripe's open banking platform, enabling businesses to securely access consumer-permissioned financial data. Our platform connects to thousands of financial institutions, powering use cases from account verification to risk assessment to personal financial management. Across the Financial Connections Engineering org, we focus on delivering high-quality, enriched bank data at scale — building the ML systems that transform raw financial data into actionable signals for both internal Stripe teams and external merchants. Our ML work spans transaction categorization, risk scoring, data enrichment, and the development of intelligent systems that improve data quality across our network. We operate at the intersection of fintech infrastructure and applied machine learning, solving problems that directly impact Stripe's ability to serve millions of businesses and consumers. What you'll do We're looking for machine learning engineers who want to build intelligent systems that provide financial data at scale. You'll play a key role in designing, training, and deploying ML models that improve the quality, accuracy, and usefulness of financial data across Stripe's ecosystem. Responsibilities Design, build, train, evaluate, deploy, and own ML models in production that improve transaction categorization, risk scoring, and data enrichment across Financial Connections Design and build large-scale ML systems that operate on diverse financial data from thousands of institutions Experiment and iterate on ML models (using tools such as PyTorch, TensorFlow, XGBoost) to achieve key business goals around data quality and accuracy Develop pipelines and automated processes to train and evaluate models in offline and online environments Integrate ML models into production systems and ensure their scalability and reliability Collaborate with product, data science, and engineering partners across Stripe to identify opportunities where ML can improve outcomes for merchants and consumers Engage with the latest ML/AI developments and take calculated risks in transforming innovative ideas into productionized solutions Mentor engineers and contribute to a strong ML engineering culture within the team Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 10+ years of industry experience building and shipping ML systems in production Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark Hands-on experience in designing, training, and evaluating machine learning models Hands-on experience in productionizing and deploying models at scale Hands-on experience in orchestrating data pipelines and efficiently leveraging large-scale datasets Strong collaboration skills and the ability to work across teams and contribute to peers' success Ability to thrive with a high level of autonomy and responsibility and an entrepreneurial mindset Preferred qualifications MS or PhD degree in ML/AI or a related field (e.g., math, physics, statistics, computer science) Experience in fintech, open banking, or financial data domains Experience with NLP, LLMs, or text classification at scale Experience in adversarial or noisy-data domains such as fraud detection, risk modeling, or data quality Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems Experience with deep learning architectures, including transformers
Engineering Manager, Machine Learning Credit Risk Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies from the world’s largest enterprises to the most ambitious startups use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team The Credit Risk team develops intelligent systems that help Stripe identify high-risk accounts, minimize credit losses, and improve profitability. Credit risk is a complex machine learning problem that requires us to distinguish emerging risk from healthy business activity while giving legitimate users a clear and reliable experience. Our team consists of machine learning engineers who build models and systems used across Stripe’s credit-risk products. We work closely with partners in Product, Data Science, Credit Strategy, Operations, and other engineering teams. Together, we help stakeholders make informed decisions and support sustainable growth wherever credit risk affects Stripe’s products. What you’ll do We’re looking for an engineering manager to lead the Credit Risk team and shape how Stripe uses machine learning to manage credit risk at scale. You’ll set the team’s technical and product direction, connect advances in machine learning to measurable business outcomes, and help engineers deliver reliable systems that balance loss prevention with the user experience. You’ll work across engineering, product, data science, and risk to identify the highest-impact opportunities and turn them into a focused roadmap. You’ll also hire and develop engineers, strengthen the team’s technical practices, and contribute to machine learning and engineering leadership across Stripe. Responsibilities Set and execute the strategy for detecting and mitigating credit risk through machine learning Own outcomes related to credit losses, profitability, detection quality, and the user experience Lead the design and delivery of reliable machine learning models, services, and decision systems Translate advances in machine learning into practical capabilities that support the team’s business goals Partner with Product, Data Science, Credit Strategy, Operations, and engineering teams to define priorities and deliver cross-functional programs Recruit, hire, and develop machine learning engineers while building an inclusive and effective team Contribute to broader engineering and machine learning initiatives as a member of Stripe’s engineering management team Who you are We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 3+ years of experience managing engineers who build and operate production machine learning systems Experience applying machine learning to complex, real-world problems and leading the technical delivery of models and supporting systems Experience setting strategy and working across engineering, product, data science, operations, and business teams to deliver measurable outcomes Experience recruiting, managing, and developing engineers in a fast-moving environment with significant autonomy Preferred qualifications Experience with credit risk, fraud detection, financial risk, trust and safety, or another domain involving decisions under uncertainty Experience balancing risk reduction with customer or user experience Experience building machine learning systems that support high-stakes, time-sensitive decisions at scale Experience setting a multi-year technical direction while delivering progress through quarterly plans Experience managing geographically distributed teams
About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Risk Detection is focused on providing a delightful experience for our merchants and minimizing friction, while ensuring the safety of our users in the financial ecosystem. Whenever the Risk team takes action on an account, we work to notify Merchants and provide clear status on what’s happening, enable guided workflows for resolving their issues, and redefine our overall Risk processes to make them as smooth as possible for good merchants. What you’ll do We’re looking for an engineering leader to lead and grow a strong team of engineers, build relationships with customers internally and externally, and champion our vision of making Stripe’s risk management a feature that attracts and retains merchants, and becomes a product differentiator. This is an exciting opportunity to partner with teams across Stripe to build the best merchant experience, and contribute directly to Stripe’s growth. Responsibilities Support the team in delivering a high level of technical quality and impact via APIs, user-facing experiences, services, and systems Recruit, hire, scale, and develop an amazing team of engineers Executing cross-functionally with leadership, product teams, infra teams & risk strategists Be actively involved in strategic direction and platform decisions that impact all of Stripe and Stripe customers Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements At least 3 years of engineering management experience Prior experience as a Machine Learning Engineer or equivalent Preferred qualifications You have managed teams that can collaborate with product teams, respond rapidly to customer needs along with building technology and capabilities that are strategic and foundational in nature Enjoy designing, measuring & improving user experience You are empathetic to customer needs but visionary enough to not just deliver a faster horse You are comfortable planning in quarters, and can set a vision for several years You are comfortable working with geographically distributed teams and remote workers
Who We Are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About The Team The Solutions Architecture (SA) organization helps Stripe's most strategic customers design and validate technical solutions that drive their business forward. Within SA, our team builds the tools, workflows, and custom technical assets that make the broader SA org more effective—turning individual ingenuity into org-wide capability. We are looking for AI Engineers who are energized by working close to the business and the users we serve. You'll operate as an embedded, high-context engineer focused on the highest-leverage opportunities across the SA org—building production-quality tooling, supporting our most strategic engagements, and shipping automation that meaningfully improves how Solutions Architects work every day. What You'll Do As an AI Engineer, you'll be embedded directly with the Solutions Architecture team—building alongside them, deeply understanding their workflows, and shipping tools and automation that permanently change how they operate. Your measure of success is SA productivity: the engagements you've accelerated, the workflows you've transformed, and the tools you've built that the org adopts as default. You'll ship code daily. You'll discover where SAs lose time and build high-impact solutions. You'll take what works for one person and scale it to work for the org. And when our most strategic customer engagements need custom technical assets, you'll build those too. This is a role for someone who wants to work at the intersection of engineering and business impact—close to customers, close to revenue, and building for people you can see using your work every day. Responsibilities Collaborate with Solutions Architects, SA leadership, Product, and Engineering to scope technical work and translate ambiguous business needs into well-defined deliverables Evaluate and integrate AI capabilities (LLMs, agents, workflow automation) where they provide genuine leverage—not for novelty, but for measurable productivity improvement Architect and build internal tools, agents, and automated workflows that accelerate SA and manager productivity across technical discovery, solution design, demoing, user engagements, territory/pipeline management, and product interlock Take high-potential tools and workflows built by SAs and managers and harden them into scalable, maintainable, production-grade solutions Identify patterns across SA workflows and proactively build solutions that address recurring friction Build custom demo environments, PoC applications, and technical assets for Stripe's most strategic customer engagements Document tools, architectures, and usage patterns so others can adopt and extend what you've built Debug, extend, and maintain backend systems across a variety of codebases and infrastructure Minimum Requirements 4+ years of experience as an engineer shipping production systems Strong backend engineering fundamentals: you can debug a failing system, trace issues across services, and reason about data flows Experience building and deploying AI agents, LLM-powered tools, or workflow automation beyond basic prompt engineering Experience scoping and delivering work with minimal oversight in a fast-moving, cross-functional environment Proficiency in at least two of: Ruby, Node.js, Python, or Next.js Familiarity with cloud infrastructure (AWS, GCP) including deployment, monitoring, and basic DevOps Experience building internal tools, developer platforms, or workflow automation Demonstrated ability to work across multiple codebases and technology stacks simultaneously Hands-on experience using AI/LLM tools in your engineering workflow—you're fluent with AI-assisted development but not dependent on it; you can reason through problems and debug without AI as a crutch Strong written and verbal communication skills; you can translate technical decisions for non-technical stakeholders and navigate cross-functional collaboration naturally Comfort with ambiguity—you can take a loosely-defined business problem, scope the engineering work, and ship iteratively without waiting for a perfect spec Preferred Qualifications Experience designing systems that non-engineers can build on top of or extend themselves (e.g., platforms, low-code frameworks, template systems) Experience in a Solutions Engineering, Sales Engineering, or GTM Engineering role—or a product engineering role where you worked closely with customers or go-to-market teams Familiarity with Stripe's products, APIs, or the payments/fintech domain Experience integrating with third-party platforms (Salesforce, Gong, etc.) Track record of building tools or systems that were adopted beyond your immediate team Background in consulting, professional services, or other roles that blend technical depth with business context Who You Are Beyond the technical requirements, we're looking for a specific kind of engineer: You want to be close to the business. You're energized by seeing your work directly impact how a sales team wins a deal or how a customer succeeds. You're a pragmatic builder. You ship working solutions quickly, iterate based on real usage, and know when "good enough now" beats "perfect later." You'd rather show a working prototype today than present a roadmap deck next quarter. You're a software engineer by practice. You can architect systems, debug production issues, write clean code, and reason about tradeoffs. AI is a tool in your belt, not a substitute for engineering judgment. You're a pattern recognizer. When you build something that works for one person, you immediately see how it generalizes. You think in reusable systems, not one-off scripts. You thrive without a traditional product team structure. No PRDs landing in your lap, no dedicated PM, no sprint ceremonies. You identify the highest-leverage problem, scope the work, and ship it. You're comfortable trading on-call rotations and rigid processes for autonomy and impact. You're a strong communicator. You can partner with SAs who are domain experts, understand their workflows deeply enough to build great tools, and explain your technical choices to leadership.
Engineering Manager, AI Conversation Platform Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team The newly formed Conversation Platform team aims to build a conversation platform for all merchants who use Stripe. We are doing so by (a) automating the easy tasks, and (b) assisting our users in the difficult tasks. Some examples include customizing the Stripe landing page to suggest bespoke integrations, allowing users to command the Stripe API in natural language, and resolving user issues automatically. We are developing RAG based systems on the latest LLMs as well as fine-tuning our own models. We’re an end-to-end team going from ideas to models to shipping in production. What you’ll do Responsibilities Driving an ambitious vision for AI/ML that benefits our users Setting the technical & process direction for the team based on business goals Brainstorm and coordinate product integrations with partner teams Proposing new ideas and building prototypes Be an integral part of a larger ML community internally & externally Hire & develop a world-class team to deliver high-quality ML systems. Coach engineers to help them grow in their careers and maintain a high bar Who you are We are looking for ML Engineering Managers who are passionate about using ML to improve products and delight customers. You have experience leading teams that develop streaming feature pipelines, build ML models, and deploy them to production, even if it involves making substantial changes to backend code. You are comfortable with ambiguity, love to take initiative, and have a bias towards action. Minimum requirements Have at least 4 years of experience managing ML teams Experience working as a Machine Learning Engineer, Applied Scientist or equivalent Individual Contributor. Lead by example in high-growth, high-impact, ambiguous environments Have experience building & shipping ML systems. Hold yourself and others to a high bar when working with production systems. Thrive in a collaborative cross-functional environment Preferred qualifications Experience in shipping LLM & RAG systems
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team Our Applied ML team aims to reform how our users interact with Stripe. We are doing so by (a) automating the easy tasks, and (b) assisting our users in the difficult tasks. Some examples include helping our users resolve issues with Stripe faster or making it easier for our users to sign up and navigate Stripe. We are using the latest LLMs as well as fine-tuning our own models. We're an end-to-end team going from ideas to models to shipping in production. You can learn more about our team’s work from this recent talk . What you’ll do As a machine learning engineer, you will be responsible for analyzing opportunities, proposing ideas, training & evaluating ML models, running experiments, and deploying everything to production. You will also have the opportunity to contribute to and influence ML architecture at Stripe as well as be a part of a larger ML community. Responsibilities Our team operates fluidly and here are some problems you may tackle: How do we evaluate a system offline & online? How do we improve performance to match (and beat) humans? How do we ensure model quality doesn’t degrade online? Does fine-tuning an LLM give us better performance? What are the right OSS and in-house platforms we should invest in? And in the process you will: Develop pipelines and automated processes to train and evaluate models in offline and online environments Integrate ML models into production systems and ensure their scalability and reliability Collaborate with product and strategy partners to propose, prioritize, and implement new product features Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions Who you are We are looking for ML Engineers who are passionate about using ML to improve products and delight customers. You have experience developing streaming feature pipelines, building ML models, and deploying them to production, even if it involves making substantial changes to backend code. You are comfortable with ambiguity, love to take initiative, and have a bias towards action. Minimum requirements Have at least 3 years of experience shipping ML systems in production Hold yourself and others to a high bar when working with production systems Take pride in taking ownership and driving projects to business impact Thrive in a collaborative environment Preferred qualifications 5+ years of experience in full time software development roles Experience shipping LLM integrations to user products with high quality Experience operating in highly ambiguous environments Knowledge about driving a hypothesis from data
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Stripe processes over $1.9T in payments volume per year, which is roughly 1.6% of the world's GDP, for millions of customers from startups to enterprises. The tremendous amount of data makes Stripe one of the best places to do machine learning. While being an integral part of almost every product line at Stripe (e.g., Payments, Radar, Capital, Billing, etc.), we have lots of exciting opportunities to innovate in ML Platform at Stripe. The ML Platform team builds the platforms and services that enable ML engineers and data scientists across Stripe to take data and build features and models from prototype to production—reliably, at low latency, and at scale. Our scope spans ML training infrastructure, model serving and deployment, feature computation and online serving, observability and monitoring, and agentic AI capabilities. We work closely with product teams, data scientists, and platform infrastructure teams to build powerful, flexible, and user-friendly systems that substantially increase ML velocity across the company. What you'll do You'll serve as a technical lead across the ML Platform space and a key contributor to the evolution of the platforms that power Stripe's ML-driven products. As a Staff Engineer, you'll make decisions with a large impact on Stripe. You'll influence our investments and strategy while making our systems more reliable, secure, and a delight to use. You'll work cross-functionally with other technical staff, data science, product, and senior leadership to increase the impact of ML at Stripe. You'll help define the long-term strategy and lead the technical direction for the next generation of ML infrastructure that powers Stripe's ML-driven products. Responsibilities Take ownership of end-to-end architecture and system design for large, complex projects across ML Platform. Define technical direction for highly ambiguous projects, transforming complex user needs into long-lasting platform strategy. Design system architectures for the most challenging ML Platform problems in one or more areas, including AI and ML workflow orchestration, scalable CPU and GPU compute infrastructure, model training, LLM fine-tuning, low-latency model inference, large-scale feature stores, real-time monitoring, and LLM and agent orchestration. Turn high-leverage ideas into tangible, robust solutions that shape platform and product roadmap, combining technical excellence with creative problem-solving. Scope and lead large projects with significant business impact, driving them from requirements through design, implementation, and production operation. Work with ML engineers, data scientists, and product teams directly to translate their needs into functional requirements and scalable technical solutions. Arbitrate critical decisions that balance competing priorities while meeting latency, reliability, cost, and security constraints. Serve as a key engineering representative, engaging senior leaders across Stripe and advising the leadership team on key technical considerations related to the end-to-end ML lifecycle. Drive cross-team technical initiatives that improve ML development velocity and MLOps maturity across the company. Mentor and grow other engineers. Serve as a role model for designing, implementing, and operating great software systems. Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 10+ years of professional software development experience, or equivalent domain expertise, with a solid background in service-oriented architecture and large-scale distributed systems. Track record of serving as a technical lead, with the ability to provide technical direction, lead multi-team initiatives, and mentor team members. Experience building and operating production ML platform in one or more areas such as model training, model serving, orchestration, or ML data systems, with requirements for performance, reliability, scalability, and cost efficiency. Strong product instincts and a deep understanding of the business context in which you operate. Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders. Demonstrated ability to work cross-functionally, collaborating effectively with ML engineers, data scientists, software engineers, product managers, and business stakeholders. The ability to thrive on a high level of autonomy and responsibility, and comfort operating in ambiguous environments. Hands-on experience using AI tools to accelerate how you work. Preferred qualifications Experience building large-scale ML training, serving, or data infrastructure for machine learning use cases, such as distributed training, model inference, feature stores, real-time feature computation, and model registries. Experience with distributed ML training systems, accelerator-backed compute, training data pipelines, experiment tracking, and model evaluation. Experience rapidly developing prototypes and iterating based on user feedback. Experience training and shipping machine learning models to production to solve critical business problems. Familiarity with LLMs, LLM application frameworks, and agentic AI patterns (e.g., tool use, multi-agent orchestration, retrieval-augmented generation). Familiarity with cloud services (e.g., AWS) and cloud-based AI and ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI). Ability to synthesize ideas across the organization while setting a compelling technical vision. Comfortable working with geographically distributed teams. Passion for side projects, open source, or self-driven technical initiatives.
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Stripe processes over $1.9T in payments volume per year, which is roughly 1.6% of the world's GDP, for millions of customers from startups to enterprises. The tremendous amount of data makes Stripe one of the best places to do machine learning. While being an integral part of almost every product line at Stripe (e.g., Payments, Radar, Capital, Billing, etc.), we have lots of exciting opportunities to innovate in ML Platform at Stripe. The ML Platform team builds the platforms and services that enable ML engineers and data scientists across Stripe to take data and build features and models from prototype to production—reliably, at low latency, and at scale. Our scope spans ML training infrastructure, model serving and deployment, feature computation and online serving, observability and monitoring, and agentic AI capabilities. We work closely with product teams, data scientists, and platform infrastructure teams to build powerful, flexible, and user-friendly systems that substantially increase ML velocity across the company. What you'll do You'll serve as a technical lead across the ML Platform space and a key contributor to the evolution of the platforms that power Stripe's ML-driven products. As a Staff Engineer, you'll make decisions with a large impact on Stripe. You'll influence our investments and strategy while making our systems more reliable, secure, and a delight to use. You'll work cross-functionally with other technical staff, data science, product, and senior leadership to increase the impact of ML at Stripe. You'll help define the long-term strategy and lead the technical direction for the next generation of ML infrastructure that powers Stripe's ML-driven products. Responsibilities Take ownership of end-to-end architecture and system design for large, complex projects across ML Platform. Define technical direction for highly ambiguous projects, transforming complex user needs into long-lasting platform strategy. Design system architectures for the most challenging ML Platform problems in one or more areas, including AI and ML workflow orchestration, scalable CPU and GPU compute infrastructure, model training, LLM fine-tuning, low-latency model inference, large-scale feature stores, real-time monitoring, and LLM and agent orchestration. Turn high-leverage ideas into tangible, robust solutions that shape platform and product roadmap, combining technical excellence with creative problem-solving. Scope and lead large projects with significant business impact, driving them from requirements through design, implementation, and production operation. Work with ML engineers, data scientists, and product teams directly to translate their needs into functional requirements and scalable technical solutions. Arbitrate critical decisions that balance competing priorities while meeting latency, reliability, cost, and security constraints. Serve as a key engineering representative, engaging senior leaders across Stripe and advising the leadership team on key technical considerations related to the end-to-end ML lifecycle. Drive cross-team technical initiatives that improve ML development velocity and MLOps maturity across the company. Mentor and grow other engineers. Serve as a role model for designing, implementing, and operating great software systems. Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 10+ years of professional software development experience, or equivalent domain expertise, with a solid background in service-oriented architecture and large-scale distributed systems. Track record of serving as a technical lead, with the ability to provide technical direction, lead multi-team initiatives, and mentor team members. Experience building and operating production ML platform in one or more areas such as model training, model serving, orchestration, or ML data systems, with requirements for performance, reliability, scalability, and cost efficiency. Strong product instincts and a deep understanding of the business context in which you operate. Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders. Demonstrated ability to work cross-functionally, collaborating effectively with ML engineers, data scientists, software engineers, product managers, and business stakeholders. The ability to thrive on a high level of autonomy and responsibility, and comfort operating in ambiguous environments. Hands-on experience using AI tools to accelerate how you work. Preferred qualifications Experience building large-scale ML training, serving, or data infrastructure for machine learning use cases, such as distributed training, model inference, feature stores, real-time feature computation, and model registries. Experience with distributed ML training systems, accelerator-backed compute, training data pipelines, experiment tracking, and model evaluation. Experience rapidly developing prototypes and iterating based on user feedback. Experience training and shipping machine learning models to production to solve critical business problems. Familiarity with LLMs, LLM application frameworks, and agentic AI patterns (e.g., tool use, multi-agent orchestration, retrieval-augmented generation). Familiarity with cloud services (e.g., AWS) and cloud-based AI and ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI). Ability to synthesize ideas across the organization while setting a compelling technical vision. Comfortable working with geographically distributed teams. Passion for side projects, open source, or self-driven technical initiatives.
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team You will be joining Stripe’s ML Foundations and Gen AI team to incubate new ML applications and improve our ML capabilities across Stripe. Our team is responsible for unlocking novel ML and LLM techniques and applications across Stripe’s product suite to drive business outcomes, as well as providing infrastructure, tooling and support for ML teams. What you’ll do As a senior product leader, you will lead a cross-functional team to define, incubate and scale new ML/AI applications across Stripe’s product suite, and drive our strategy and roadmap for ML/AI infrastructure powering all of Stripe’s teams. You will work closely with product leaders across business units to define and deliver on an AI-centric product strategy, launching new applications that drive incremental business outcomes. At the same time, you will be advancing our core AI technology stack to empower teams across Stripe to infuse their scenarios with Agents and agentic capabilities, with API support for agent quality and continuous improvement. Responsibilities Develop and execute on the Stripe-wide strategy for new ML/AI applications across our product suite Evaluate and align on areas of investment for ML/AI applications in collaboration with product leaders across the company Work with cross-functional teams to execute on the roadmap and launch successful new ML/AI applications Communicate clearly and crisply with leadership stakeholders and drive alignment across multiple teams Develop and execute on a strategy for advancing Stripe’s ML/AI infrastructure and tooling Who you are We’re looking for someone who meets the requirements below, and has a passion for AI to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 7+ years of experience delivering highly successful and innovative software products which are ML powered Solid understanding of ML and applied AI tech stacks Demonstrated ability to influence company level strategy and work with business leaders to execute on the transformation You push the pace. You take blame and pass the praise. People love working with you. Proven ability to lead teams and work cross-functionally in a highly collaborative environment. Ability to analyze and use quantitative and qualitative data to inform decisions. A deep understanding and empathy for consumer and business users — you love building products that make our customers feel joy, delight and trust. Relentlessly drives product quality Capable of working on both 1P and 3P products
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As an Applied AI Engineer at Anthropic, you will be a trusted technical advisor helping tech-forward companies build on the Claude Developer Platform as they grow and scale. You'll lead deep technical engagements, partnering directly with engineering teams to help them develop their products on top of Claude - advising on implementation, agent design, and working alongside them to build at the frontier of AI. Working closely with Applied AI Architects, Account Executives, and Anthropic's Product and Engineering teams, you'll guide customers through deep technical engagements. You'll leverage your AI engineering expertise to develop custom evaluation frameworks, design scalable architectures, and create the technical resources that enable tech-forward businesses to succeed with Claude. Responsibilities Serve as a deep technical advisor to high-growth, tech-forward companies, working alongside them to build innovative use cases that push the boundaries of AI Work hands-on with customer engineering teams: pair programming, architecture reviews, and code contributions that accelerate their development Develop prototypes and technical documentation—including evaluation suites, AI engineering techniques, and architecture diagrams—that enable customers to build and scale with Claude Collaborate closely with Applied AI Architects to maintain context and continuity across customer engagements Identify patterns across engagements and contribute insights back to Product, Engineering, and the broader Applied AI team Create technical content for developer audiences including documentation, tutorials, and sample code Foster community engagement through hackathons, webinars, technical office hours, and developer-focused events Travel to customer sites for workshops, implementation support, and relationship building You may be a good fit if you have 4+ years of experience as a Software Engineer, Forward Deployed Engineer, or technical founder Experience in a customer-facing or pre-sales technical role - partnering with customers to scope, design, and deliver solutions Production experience building LLM-powered applications, including prompting, context engineering, agent architectures, evaluation frameworks, and deployment at scale Strong programming skills with proficiency in Python and experience building production applications Experience working with high-growth, tech-forward companies Ability to context-switch across industries (healthcare, fintech, etc.) and use cases Builder credibility that earns trust with technical founders and engineering leaders - you've shipped products and can speak from experience Strong technical communication skills with the ability to translate complex AI concepts into architectural decisions and actionable implementation plans Experience facilitating technical workshops, hackathons, or developer-focused events Passion for making powerful technology safe and beneficial The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: £225,000 — £255,000 GBP Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! At Figma we believe design doesn’t end in a file or with your designer - it includes everything that goes into the product you ship: the production code that ties it all together, the systems and developer tools that make that code reliable, the context you provide to our AI agents, and the documentation that keeps everyone on the same page. The Roundtripping Area at Figma is redefining how Designers, PMs and Engineers collaborate. We are responsible for agentic workflows enabling ideation and prototyping on production codebases as well as accelerating the journey from design to code. In 2023, we launched Dev Mode, a suite of features that give developers everything they need to navigate design files and transform designs into code. In 2025, we introduced Figma’s MCP, accelerating how ideas get to production. Looking to the future, we aim to further reduce the barriers between design to code and code to design allowing ideation, prototyping and productionalising to happen seamlessly where it most makes sense. Our Roundtripping team is expanding, and we’re hiring AI Product Engineers across multiple levels in the UK. We’re looking for people who have built generative AI products and are eager to lead AI efforts end-to-end, from early ideas to production. Join us in shaping the future of AI at Figma. What you’ll do at Figma: Build and evolve Dev Mode our MCP tools and Make, Figma’s leading tools for dev/design collaboration Take part in building new 0→1 products within the agentic coding space Collaborate with designers, PMs, and other engineers craft high quality products Be a mentor to, and be mentored by, exceptional peers across engineering, product and design Build strong personal connections with your teammates and help shape Figma’s culture We’d love to hear from you if you have: 3+ years of leading large scale engineering projects with high impact Working with user facing LLM and agentic product features Experience curating and developing eval sets and LLMs as judges Attention to detail, quality, and craftsmanship High velocity and a focus on user impact While it’s not required, it’s an added plus if you also have: Driving XFN projects collaborating with Product, Design, Data and Research Shipping user-facing features or products as a full-stack developer Demonstrated strong fluency with React and Typescript Experience communicating and working across functions to proactively drive solutions Trained or fine-tuned LLMs At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you're excited about this role but your past experience doesn't align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. At Figma we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our product and our community to flourish. Figma is an equal opportunity workplace - we are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity/expression, veteran status , or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. We will work to ensure individuals with disabilities are provided reasonable accommodation to apply for a role, participate in the interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please reach out to accommodations-ext@figma.com . These modifications enable an individual with a disability to have an equal opportunity not only to get a job, but successfully perform their job tasks to the same extent as people without disabilities. Examples of accommodations include but are not limited to: Holding interviews in an accessible location Enabling closed captioning on video conferencing Ensuring all written communication be compatible with screen readers Changing the mode or format of interviews To ensure the integrity of our hiring process and facilitate a more personal connection, we require all candidates keep their cameras on during video interviews. Additionally, if hired you will be required to attend in person onboarding. By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with Figma's Candidate Privacy Notice .
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As a Staff Software Engineer on the GTM AI Engineering (Claudification) team, you will build the agents and AI systems that run Anthropic's own go-to-market work. Our sellers already work alongside agents every day. You will take things to the next step and build agents that run complete autonomous motions across areas like inbound, outbound, and pipeline management. In addition, you’ll build eval frameworks that prove those agents are ready for customer-facing work and are driving value. This is a senior role where you’ll drive technical direction for agents and evals across our team. Working closely with sellers, RevOps, and our platform engineering partners, you'll own projects from first prototype through production operation. You'll combine full-stack engineering (MCP servers, agentic systems, web applications, etc.) with hands-on evaluation work (behavior benchmarks, production monitoring, ROI measurement), and help architect the shared platforms that builders from across our go-to-market org contribute to. You've worked in cultures of analytical rigor before, and you're eager to help shape the norms and best practices of a growing AI engineering function at a pivotal moment in the company's growth. Key responsibilities Build and operate autonomous agents that run go-to-market motions end to end, across areas like inbound, outbound, pipeline management, and customer engagement Design the human oversight for each motion: approval gates, handoffs, and escalation paths that keep sellers in control Develop evaluation frameworks for agent behavior, and run them in development and in production Instrument model and tool calls in production, and build the observability and measurement that ties agent actions to pipeline and revenue Ship MCP servers, agent skills, and web applications that connect to systems like our CRM, communication tools, and data warehouse Set the technical direction for how we build, evaluate, and operate agents across the team Architect shared codebases that builders from across go-to-market contribute to, setting the conventions and review practices that keep quality high Work directly with sellers to ground agent designs in real workflows, and iterate based on what you observe Identify repeatable patterns and contribute insights back to Anthropic's Product and Engineering teams Maintain strong knowledge of the latest developments in LLM capabilities, agent frameworks, and evaluation techniques Minimum qualifications Strong programming skills in Python or TypeScript, with experience building and operating production applications Production experience with LLMs, including context engineering, agent development, MCP development, tool use, and evaluation frameworks Experience using evals and transcript analysis to find and fix real problems in an LLM system Working fluency with data, including SQL Ability to navigate ambiguity and ship without a spec, finding simple solutions to complex problems Passion for advancing safe, beneficial AI, and care for the people who use what you build Preferred qualifications 8+ years in roles such as software engineer, ML engineer, or forward deployed engineer. Former technical founders are encouraged to apply Experience with the Claude Code and the Claude Agent SDK Experience with go-to-market systems (CRM, sales engagement, enrichment, conversation intelligence) or time working closely with a revenue team Experience growing a codebase that many people contribute to, inner-source or open-source Applied ML and experimentation background: A/B testing, propensity models, recommendations, or causal analysis Exceptional communication skills to convey technical concepts to non-technical partners with low ego Representative projects (Illustrative of the kind of work, not a project list.) Build an agent that takes a routine sales workflow from first signal to a drafted, human-reviewed action Stand up the eval suite for an agent: seed scenarios, scoring rubrics, and regression runs on every change Ship an MCP server that gives sellers and their agents governed access to a core revenue system Design a shared repository where go-to-market builders publish agents and skills, with the tests and review rules that keep it healthy Build a predictive model that explains itself, so an agent can tell a seller why it suggests an action The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About Beneficial Deployments Beneficial Deployments ensures AI reaches and benefits the communities that need it most. We partner with nonprofits, foundations, and mission-driven organizations to deploy Claude in education, global health, economic mobility, and life sciences- focusing on raising the floor for those who need it most. About the Role We're looking for an Applied AI Engineer to join our Beneficial Deployments team, focused on maximizing the impact of Claude in the life sciences. Our goal is ambitious: accelerate scientific progress from R&D through translation. That means making Claude the go-to tool for the life sciences ecosystem from early discovery in academia to paradigm shifting biotech to reimagining pharma pipelines - and building the technical infrastructure to back that up. You'll work directly with flagship research partners like The Howard Hughes Medical Institute (HHMI) and The Allen Institute, embedded in their scientific workflows. This isn't consulting from the outside - you'll be building alongside their engineers, prototyping agents that fit into real research pipelines, and developing the ecosystem-level tooling (MCP servers, benchmarks, reusable agent skills) that extends Claude's usefulness across the broader life sciences community. This role will be part of the founding Beneficial Deployments Applied AI team focused on bringing life sciences closer to the frontier. Responsibilities Partner deeply with flagship life sciences research institutions - understand their scientific workflows end-to-end, build hands-on with their engineering teams, and help take projects from early exploration to production systems integrated into how they do science day-to-day. Develop reusable ecosystem infrastructure, like MCP servers for domain-specific data sources (genomics platforms, literature databases, experimental repositories), instruments, scientifically-grounded benchmarks, and agent skills that other institutions can adopt without starting from scratch. Identify what's actually hard about deploying AI in life sciences (heterogeneous data, auditability requirements, the prototype-to-trust gap) and feed those findings back to product, engineering, and research. Create technical content and documentation that lets partners self-serve, so what works for one institution can scale globally without the same level of hand-holding. You Might Be a Good Fit If You Have: Deep research experience in life sciences, biomedical research, or scientific computing. Bonus if you've studied genomics, neuroscience, or drug discovery specifically and are comfortable getting deeply technical with academics. Experience building LLM-powered tools or applications: prompting, context engineering, agent architectures, evaluation frameworks. Builder credibility from shipping production code as a software engineer, forward-deployed engineer, or technical founder. A scrappy mentality–comfortable wearing multiple hats, building from scratch, driving clarity in ambiguous situations, and doing whatever it takes to further the mission. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: £225,000 — £255,000 GBP Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. À propos du poste En tant qu'ingénieur·e en IA appliquée au sein de notre équipe Entreprise, votre rôle est de guider certaines des plus grandes entreprises de France vers la frontière de l'IA et de les aider à déployer Claude dans leurs opérations, leurs produits et leurs méthodes de travail. Vous deviendrez le·la spécialiste incontournable de la plateforme Claude et le·la conseiller·ère technique de confiance de vos clients, en conseillant leurs équipes d'ingénierie sur l'architecture, la conception d'agents et l'évaluation, et en travaillant avec les dirigeants et les cadres supérieurs qui sponsorisent ces programmes pour démontrer la valeur ajoutée de Claude et les aider à prendre une décision éclairée quant à l'adoption de Claude. Les transactions avec les entreprises sont différentes. Les cycles d'approvisionnement sont plus longs, davantage de parties prenantes sont impliquées, et la réussite dépend de la capacité à relier le travail technique aux résultats qui importent pour le conseil d'administration. Vous serez tout aussi à l'aise pour conseiller une équipe d'ingénierie sur l'évaluation et l'architecture que pour présenter un dossier de valeur à un CIO ou un CTO, et vous porterez le fil technique à travers chaque étape d'un engagement s'étalant sur plusieurs trimestres. Une grande partie de ce travail se déroule en français, avec des cadres dirigeants et des équipes d'ingénierie françaises, c'est pourquoi un français natif ou courant est essentiel pour ce poste. Vous travaillerez en étroite collaboration avec les responsables de comptes Entreprise et les architectes IA appliquée pour accompagner chaque client depuis la première conversation, à travers l'évaluation technique, la création de valeur et les achats, jusqu'à la décision de développer sur Claude. Une fois leur décision prise, vous préparerez leur partenaire d'intégration système à assurer la mise en œuvre, en définissant ensemble l'architecture cible et les critères de réussite, et vous resterez proche du client en tant que conseiller·ère technique sur Claude pendant tout le déploiement, tandis que le partenaire se chargera de la construction. En travaillant aux côtés des équipes Produit et Ingénierie d'Anthropic, vous mettrez à profit votre connaissance de la plateforme Claude pour conseiller sur l'évaluation, recommander des architectures répondant aux exigences de sécurité et d'échelle des entreprises, et créer les ressources techniques et commerciales qui aident les grandes organisations à réussir avec Claude. Responsabilités Travailler en partenariat avec les responsables de comptes et les architectes en IA appliquée pour accompagner les clients entreprises en France depuis le premier échange jusqu'à la décision d'adopter Claude, en passant par l'évaluation technique, la validation de la valeur et le processus d'achat Piloter le volet technique de chaque opportunité jusqu'à cette décision, et rester le·la conseiller·ère technique du client sur Claude pendant son déploiement Préparer les clients et leurs partenaires d'intégration de systèmes à assurer la livraison : convenir de l'architecture cible, définir les critères de réussite, et rester disponible pour conseiller l'équipe de livraison au fur et à mesure de l'avancement de la construction Guider les clients grandes entreprises vers l'avant-garde de l'IA, en les conseillant sur la manière de déployer Claude dans leurs opérations, leurs produits et leurs méthodes de travail Aider les équipes d'ingénierie des clients à voir comment elles pourraient développer sur Claude, en couvrant l'architecture, la conception d'agents et l'évaluation, afin qu'elles puissent prendre une décision en toute confiance Élaborer et présenter le dossier de valeur commerciale de Claude auprès des dirigeants et des cadres supérieurs, en français et en anglais, en associant les capacités techniques à des résultats mesurables tels que les coûts, les revenus, les risques et le délai de mise sur le marché Guider les clients à travers de longs cycles d'approvisionnement et d'adoption impliquant de multiples parties prenantes, notamment les évaluations techniques, les preuves de concept, les revues de sécurité et d'architecture, jusqu'à la définition d'une feuille de route claire vers la mise en production Travailler avec les responsables de comptes sur la stratégie de compte, la qualification des opportunités commerciales et les plans d'engagement des dirigeants, et avec les architectes en IA appliquée pour assurer la continuité et le partage du contexte à travers chaque engagement Organiser des briefings exécutifs, des ateliers techniques et des sessions de formation destinés à la fois aux dirigeants et aux équipes d'ingénierie Naviguer au sein des exigences de l'entreprise en matière de sécurité, de conformité, de résidence des données et de gouvernance, en collaborant avec les équipes internes d'Anthropic si nécessaire Identifier les modèles à travers les engagements auprès des entreprises et transmettre les informations aux équipes Produit, Ingénierie et à l'équipe IA appliquée élargie Se rendre sur les sites des clients en France pour des réunions de direction, des ateliers et l'établissement de relations Vous conviendrez particulièrement si vous avez Le français comme langue maternelle ou courant, écrit et parlé, car vous travaillerez au quotidien avec des clients entreprises français, aussi bien au niveau des dirigeants qu'au niveau ingénierie Plus de 6 ans d'expérience en tant qu'ingénieur·e logiciel, architecte de solutions, ingénieur·e en déploiement avancé ou fondateur·rice technique Une expérience dans un poste d'avant-vente ou de conseil technique auprès de grandes entreprises, idéalement sur le marché français, y compris la définition du périmètre et la conception de solutions complexes Une expérience dans la vente basée sur la valeur, ce qui signifie que vous êtes en mesure d'élaborer un dossier commercial, de quantifier l'impact et de le présenter à des décideurs de haut niveau L'aisance à présenter et à établir des relations avec des parties prenantes de niveau C et VP Une expérience dans la prise en charge de la réussite technique des transactions avec les entreprises : évaluations, preuves de concept et audits techniques préalables avec les équipes d'ingénierie, de sécurité, juridique et des achats Une expérience en production avec des applications propulsées par des LLM, incluant le prompting, l'ingénierie du contexte, les architectures d'agents, les cadres d'évaluation et le déploiement à grande échelle Une expérience pratique en ingénierie, incluant la conception et le déploiement d'applications en production, afin de pouvoir échanger avec les responsables techniques et leurs équipes Une familiarité avec les exigences en matière de sécurité, de conformité et d'architecture d'entreprise, et une expérience dans la résolution de ces problématiques avec les équipes clientes La capacité à expliquer clairement des concepts complexes d'IA à des publics techniques et dirigeants, et à naviguer entre des secteurs tels que les services financiers, les soins de santé, le commerce de détail et l'industrie manufacturière Un intérêt marqué pour la mise en place de technologies puissantes sûres et bénéfiques About the role As an Applied AI Engineer on our Enterprise team, your role is to guide some of France's largest businesses to the frontier of AI and help them deploy Claude into their operations, their products, and their ways of working. You will become the go-to Claude Platform specialist and trusted technical advisor for your customers, advising their engineering teams on architecture, agent design, and evaluation, and working with the C-level and senior executives who sponsor these programmes to show the business value Claude can create and help them reach a confident decision to build on Claude. Enterprise deals are different. Procurement cycles are longer, more stakeholders are involved, and success depends on being able to connect the technical work to the outcomes the boardroom cares about. You will be as comfortable advising an engineering team on evaluation and architecture as you are presenting a value case to a CIO or CTO, and you will carry the technical thread across every stage of a multi-quarter engagement. Much of this work happens in French, with French executives and engineering teams, so native or fluent French is essential for the role. You will partner closely with Enterprise Account Executives and Applied AI Architects to take each customer from first conversation, through technical evaluation, proof of value and procurement, to the decision to build on Claude. Once they have decided, you set their systems integration partner up to deliver, agreeing the target architecture and what good looks like, and you stay close as the customer's technical advisor on Claude throughout deployment, while the partner does the build. Working alongside Anthropic's Product and Engineering teams, you will use your knowledge of the Claude Platform to advise on evaluation, recommend architectures that meet enterprise security and scale requirements, and create the technical and business resources that help large organisations succeed with Claude. Responsibilities Partner with Enterprise Account Executives and Applied AI Architects to take enterprise customers in France from first conversation, through technical evaluation, proof of value and procurement, to the decision to build on Claude Lead the technical side of each opportunity through to that decision, and remain the customer's technical advisor on Claude as they deploy Set customers and their systems integration partners up to deliver: agree the target architecture, define what good looks like, and stay on hand to advise the delivery team as the build progresses Guide enterprise customers to the frontier of AI, advising them on how to deploy Claude into their operations, their products, and their ways of working Help customer engineering teams see how they would build on Claude, covering architecture, agent design and evaluation, so they can make a confident decision Build and present the business value case for Claude with C-level and senior executives, in French and English, linking technical capabilities to measurable outcomes such as cost, revenue, risk, and time to market Guide customers through long, multi-stakeholder procurement and adoption cycles, including technical evaluations, proofs of concept, security and architecture reviews, and a clear path to production Work with Account Executives on account strategy, deal qualification, and executive engagement plans, and with Applied AI Architects to keep context and continuity across every engagement Run executive briefings, technical workshops, and enablement sessions for both leadership and engineering audiences Navigate enterprise requirements around security, compliance, data residency, and governance, working with Anthropic's internal teams where needed Identify patterns across enterprise engagements and feed insights back to Product, Engineering, and the wider Applied AI team Travel to customer sites across France for executive meetings, workshops, and relationship building You may be a good fit if you have Native or fluent French, written and spoken, as you will work day to day with French enterprise customers at both executive and engineering level 6+ years of experience as a Software Engineer, Solutions Architect, Forward Deployed Engineer, or technical founder Experience in a pre-sales or technical advisory role selling into large enterprises, ideally in the French market, including scoping and designing complex solutions Experience with value-based selling, meaning you can build a business case, quantify impact, and present it to senior decision makers Comfort presenting to, and building relationships with, C-level and VP-level stakeholders Experience owning the technical win in enterprise deals: evaluations, proofs of concept and technical due diligence with engineering, security, legal and procurement teams Production experience with LLM-powered applications, including prompting, context engineering, agent architectures, evaluation frameworks, and deployment at scale A hands-on engineering background, including building and deploying production applications, so you can relate to engineering leaders and their teams Familiarity with enterprise security, compliance, and architecture requirements, and experience working through them with customer teams The ability to explain complex AI concepts clearly to both technical and executive audiences, and to move between industries such as financial services, healthcare, retail, and manufacturing A passion for making powerful technology safe and beneficial The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: €220.000 — €235.000 EUR Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As an Applied AI Engineer on our Enterprise team, your role is to guide some of Germany's largest businesses to the frontier of AI and help them deploy Claude into their operations, their products, and their ways of working. You will become the go-to Claude Platform specialist and trusted technical advisor for your customers, advising their engineering teams on architecture, agent design, and evaluation, and working with the C-level and senior executives who sponsor these programmes to show the business value Claude can create and help them reach a confident decision to build on Claude. Enterprise deals are different. Procurement cycles are longer, more stakeholders are involved, and success depends on being able to connect the technical work to the outcomes the boardroom cares about. You will be as comfortable advising an engineering team on evaluation and architecture as you are presenting a value case to a CIO or CTO, and you will carry the technical thread across every stage of a multi-quarter engagement. Much of this work happens in German, with German executives and engineering teams, so native or fluent German is essential for the role. You will partner closely with Enterprise Account Executives and Applied AI Architects to take each customer from first conversation, through technical evaluation, proof of value and procurement, to the decision to build on Claude. Once they have decided, you set their systems integration partner up to deliver, agreeing the target architecture and what good looks like, and you stay close as the customer's technical advisor on Claude throughout deployment, while the partner does the build. Working alongside Anthropic's Product and Engineering teams, you will use your knowledge of the Claude Platform to advise on evaluation, recommend architectures that meet enterprise security and scale requirements, and create the technical and business resources that help large organisations succeed with Claude. Responsibilities Partner with Enterprise Account Executives and Applied AI Architects to take enterprise customers in Germany from first conversation, through technical evaluation, proof of value and procurement, to the decision to build on Claude Lead the technical side of each opportunity through to that decision, and remain the customer's technical advisor on Claude as they deploy Set customers and their systems integration partners up to deliver: agree the target architecture, define what good looks like, and stay on hand to advise the delivery team as the build progresses Guide enterprise customers to the frontier of AI, advising them on how to deploy Claude into their operations, their products, and their ways of working Help customer engineering teams see how they would build on Claude, covering architecture, agent design and evaluation, so they can make a confident decision Build and present the business value case for Claude with C-level and senior executives, in German and English, linking technical capabilities to measurable outcomes such as cost, revenue, risk, and time to market Guide customers through long, multi-stakeholder procurement and adoption cycles, including technical evaluations, proofs of concept, security and architecture reviews, and a clear path to production Work with Account Executives on account strategy, deal qualification, and executive engagement plans, and with Applied AI Architects to keep context and continuity across every engagement Run executive briefings, technical workshops, and enablement sessions for both leadership and engineering audiences Navigate enterprise requirements around security, compliance, data residency, and governance, working with Anthropic's internal teams where needed Identify patterns across enterprise engagements and feed insights back to Product, Engineering, and the wider Applied AI team Travel to customer sites across Germany for executive meetings, workshops, and relationship building You may be a good fit if you have Native or fluent German, written and spoken, as you will work day to day with German enterprise customers at both executive and engineering level 6+ years of experience as a Software Engineer, Solutions Architect, Forward Deployed Engineer, or technical founder Experience in a pre-sales or technical advisory role selling into large enterprises, ideally in the German market, including scoping and designing complex solutions Experience with value-based selling, meaning you can build a business case, quantify impact, and present it to senior decision makers Comfort presenting to, and building relationships with, C-level and VP-level stakeholders Experience owning the technical win in enterprise deals: evaluations, proofs of concept and technical due diligence with engineering, security, legal and procurement teams Production experience with LLM-powered applications, including prompting, context engineering, agent architectures, evaluation frameworks, and deployment at scale A hands-on engineering background, including building and deploying production applications, so you can relate to engineering leaders and their teams Familiarity with enterprise security, compliance, and architecture requirements, and experience working through them with customer teams The ability to explain complex AI concepts clearly to both technical and executive audiences, and to move between industries such as financial services, healthcare, retail, and manufacturing A passion for making powerful technology safe and beneficial Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.