Machine Learning Engineer Jobs
Browse open Machine Learning Engineer positions aggregated from verified tech companies. Optimize your resume for these roles using our free analyzer and resume examples.
Staff Machine Learning Engineer, Personalization
Machine Learning Engineering Manager, Personalization
Software Engineer, Machine Learning Infrastructure
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 $1T in payments volume per year, which is roughly 1% of the world’s GDP. The tremendous amount of data makes Stripe one of the best places to do machine learning. The ML Infra team builds services and tools that power every step in the ML lifecycle, including data exploration, feature generation, experimentation, training, deploying, serving ML models, and building LLM applications. With the phenomenal developments happening in the field of AI, we are positioned to accelerate the adoption of AI/ML across all parts of the company by building highly scalable and reliable foundational infrastructure. What you’ll do You will work closely with machine learning engineers, data scientists, and product engineering teams to enable seamless end-to-end experience in building solutions across data, analytics, and AI/ML platforms. You will build the next generation of ML Infra services and major new capabilities that substantially improve ML development velocity and MLOps maturity across the company. Responsibilities Designing and building scalable, reliable, and secure services for notebooks, ML model training, experimentation, serving, and LLM applications across multiple regions. Creating services and libraries that enable ML engineers at Stripe to seamlessly transition from experimentation to production across Stripe’s systems. Working directly with product teams and ML engineers to improve their day-to-day productivity. Taking ownership of and finding solutions for technical and product challenges by working with a diverse set of systems, processes, and technologies. Who you are We’re looking for people with a strong background or interest in building successful products or systems; you’re passionate about solving business problems and making impact, you are comfortable in dealing with lots of moving pieces; and you’re comfortable learning new technologies and systems. You are comfortable working with other Stripe teams across the US and Canada. Minimum requirements 2+ years of professional software development experience with a solid background on service oriented architecture and large-scale distributed systems Experience working through the full life cycle of software development, from talking to users, to design and implementation, to testing and deployment, to operations Experience working on production ML platforms, MLOps solutions, or building LLM applications Experience running operations for high availability, low latency systems Experience partnering with other teams to drive business outcomes A sense of pragmatism: you know when to aim for the ideal solution and when to adjust course Preferred qualifications Experience building and shipping production AI agents Familiarity with the LLMs and LLM Frameworks Experience training and shipping machine learning models to production to solve critical business problems
Machine Learning Engineering Manager - Fraud Detection
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
Machine Learning Engineer, Radar
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 Radar ML team builds the fraud detection models that protect Stripe's $1.9 trillion payment network from fraud. The team owns 10+ real-time deep learning models that must constantly evolve to stay ahead of fraudsters. Each ML improvement translates directly into dollar impact for Stripe and its users. The team's models also power the Radar product suite that tens of thousands of businesses use to screen payments and manage fraud. Radar is growing fast, and the team is actively building new products like defenses against AI token theft, free trial abuse, and programmatic attacks. What you’ll do In this role, you will own ML work across the full lifecycle: researching new fraud patterns, building and deploying models, and sharing results directly with top Stripe customers. You will have opportunities to optimize Stripe’s most intensive ML models, and opportunities to ship 0-to-1 products from scratch. Responsibilities Build, train, evaluate, and deploy ML models that detect fraud across Stripe’s global payments network Research emerging fraud patterns like token theft and develop ML solutions to address them Apply advances in deep learning to improve model quality and detection rates at scale Co-build new fraud and abuse products directly with top users 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 training, evaluating, and deploying ML models in a production environment Proficiency in Python and common data and ML frameworks like SQL, Spark, and PyTorch Strong knowledge of production ML systems; and data analysis, statistics, and experiment design fundamentals Active interest in the latest ML developments, and how they can be leveraged to solve business problems Preferred qualifications Experience building and optimizing real-time, low-latency ML infrastructure at scale Strong software engineering skills and ability to design ML solutions through entire product stack Experience applying ML to fraud detection, risk modeling, or a closely related domain Experience designing ML products used by millions of users
ML Engineer Manager, AI Conversation Platform
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
Staff Product Manager, ML Foundations and GenAI
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
Staff Software Engineer, Machine Learning 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 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.
Staff Software Engineer, Machine Learning 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 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.
AI Engineer
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.
Machine Learning Engineer
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
Machine Learning Engineer, Capital Underwriting
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 Capital provides access to fast, flexible financing to small-and-medium businesses on Stripe to accelerate their growth, and we lent over $1B in 2024. Businesses use the funds for marketing, team growth, geographic expansion, working capital, new equipment purchases, and much more. Machine learning is core to Stripe Capital’s business—we use information about businesses from their activity within and outside of Stripe and our models to automatically underwrite uniquely tailored financing offers to their needs, which banks are often unable to do. We are doing so through models with an established performance history, data infrastructure that is Stripe scale, and a strong feedback loop that includes explainability, anomaly detection and a risk portfolio management layer. We're an end-to-end team going from ideas to models to shipping in production. What you’ll do As a machine learning engineer for Stripe Capital, you'll be responsible for designing, building, training, evaluating, deploying, and owning ML models in production with the goals of providing financing opportunities to as many users as possible while satisfying financial performance goals. You'll work closely with software engineers, data scientists, product managers, and risk managers to operate Stripe’s ML powered systems, features, and products. You'll also contribute to and influence ML architecture at Stripe and be a part of a larger ML community. Responsibilities Design state-of-the-art ML models and large scale ML systems for underwriting and portfolio management for Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering constraints Design systems to speed up the time from idea to deployment of new models Experiment and iterate on ML models (using tools such as PyTorch and TensorFlow) to achieve key business goals and drive efficiency 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 building ML systems that touch the lives of millions. You have experience developing efficient feature pipelines, building advanced ML models, and deploying them to production. You are comfortable with ambiguity, love to take initiative, have a bias towards action, and thrive in a collaborative environment. We’re looking for someone who can bring new ideas to the table on building models able to push the state of the art at Stripe, especially within the regulatory and operational constraints of a financing business. Minimum requirements 5+ 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 complicated data pipelines and efficiently leveraging large-scale datasets Preferred qualifications MS/PhD degree in ML/AI or related field (e.g. math, physics, statistics) Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems Experience in adversarial domains such as Lending, Trading, Fraud Experience with Deep Learning including the latest architectures such as transformers, test-time compute, reinforcement learning
Software Engineer, Machine Learning
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! Figma is seeking a versatile and experienced Machine Learning / AI Engineer to join our growing AI team, working at the intersection of applied machine learning, infrastructure, and product innovation. Whether you’re building intelligent search systems, crafting scalable data pipelines, or enhancing AI-powered creativity tools, your work will drive user productivity, shape new product experiences, and advance the state of AI at Figma. You’ll collaborate closely with engineers, researchers, designers, and product managers across multiple teams to deliver high-quality ML-driven features and infrastructure. This is a high-impact, cross-functional role where you’ll shape both foundational systems and user-facing capabilities. This is a full time role that can be held from one of our US hubs or remotely in the United States. What you’ll do at Figma: Design, build, and productionize ML models for Search, Discovery, Ranking, Retrieval-Augmented Generation (RAG), and generative AI features. Build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows. Collaborate with AI researchers to iterate on datasets, evaluation metrics, and model architectures to improve quality and relevance. Work with product engineers to define and deliver impactful AI features across Figma’s platform. Partner with infrastructure engineers to develop and optimize systems for training, inference, monitoring, and deployment. Explore new ideas at the edge of what’s technically possible and help shape the long-term AI vision at Figma. We’d love to hear from you If you have: 5+ years of industry experience in software engineering, with 3+ years focused on applied machine learning or AI. Strong experience with end-to-end ML model development, including training, evaluation, deployment, and monitoring. Proficiency in Python and familiarity with ML libraries like PyTorch, TensorFlow, Scikit-learn, Spark MLlib, or XGBoost. Experience designing and building scalable data and annotation pipelines, as well as evaluation systems for AI model quality. Experience mentoring or leading others and contributing to a culture of technical excellence and innovation. While not required, It’s an added plus if you also have: Familiarity with search relevance, ranking, NLP, or RAG systems. Experience with AI infrastructure and MLOps, including observability, CI/CD, and automation for ML workflows. Experience working on creative or design-focused ML applications. Knowledge of additional languages such as C++ or Go is a plus, but not required. A product mindset with the ability to tie technical work to user outcomes and business impact. Strong collaboration and communication skills, especially when working across functions (engineering, product, research). 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. Pay Transparency Disclosure Job level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), market demands, and specific work location. Figma offers equity to employees, as well as a competitive package of additional benefits, including health, dental, and vision coverage; retirement benefits with company contributions; parental leave and reproductive or family planning support; mental health and wellness benefits; and paid time off. Figma provides paid sick leave, holidays, and other leave benefits in compliance with applicable federal, state, and local laws, including the requirements of the Washington Minimum Wage Act and related regulations. Exempt employees are eligible for employer‑provided paid flexible PTO in addition to flexible paid sick leave. PTO is subject to manager approval. Additional benefits may include company recharge days, cell phone and home internet reimbursements, and a number of lifestyle spending accounts. Figma also offers sales incentive compensation for most sales roles and an annual bonus plan for eligible non-sales roles. All compensation and benefits are subject to applicable plan terms and may be modified by Figma at any time, consistent with applicable law. Annual Base Salary Range: $153,000 — $376,000 USD 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 .