Machine Learning Engineer, Radar

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.

The Radar ML team builds the machine learning systems that power Stripe’s Radar fraud and abuse product suite. Our models make real-time decisions across a significant share of internet commerce - helping businesses stop fraud, increase authorization, and grow safely. By turning economy-scale datasets into compelling ML products, the team directly improves customer outcomes and drives meaningful revenue for Stripe.

The team is growing, and expanding into multiple new areas. We are building abuse protections across the full customer life-cycle, adaptive and customizable fraud systems for enterprises, and risk intelligence for 50+ global payment methods. 

You will own ML problems from conception to production: analyzing new fraud and abuse patterns, exploring large-scale datasets, experimenting with model architectures, designing evaluations, deploying models, and monitoring performance.

Your work may include developing transformer architectures for payment data, building graph-powered intelligence across consumers, leveraging LLMs to detect emerging attacks, or rapidly prototyping new protections with frontier AI labs. 

You will have opportunities to ship 0→1 products, and to improve mission-critical ML models operating at Stripe scale. 

• →Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe’s global network.
• →Investigate emerging fraud and abuse patterns, identify where ML can help, and rapidly develop effective solutions.
• →Design features, datasets, evaluation methods, and experiments for noisy, delayed, and adversarial environments.
• →Improve model architectures, training methods, and production systems to increase model quality, coverage, and adaptability.
• →Monitor production behavior and improve models as data, customer needs, and attack patterns change.
• →Balance model quality with legitimate-user impact, latency, reliability, and operational complexity.
• →Partner with engineering, product, data science, risk experts, and leading Stripe users to turn modeling improvements into differentiated Radar products.
• →Contribute to reusable tools and AI-assisted workflows that help the team develop and operate ML systems more effectively.

You are a fast, pragmatic problem solver with deep ML expertise and a bias toward action. You are excited to work at the frontier of payments and AI - turning advances in machine learning into production systems that address new forms of fraud and abuse. You’ve built ML models and shepherded them to production, landing impact for users.

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.

• 2+ years of experience developing, evaluating, and deploying machine learning models in a production environment.
• Strong foundations in machine learning, data analysis, statistics, model evaluation, and experiment design.
• Strong programming and software-engineering skills, with experience writing tested, maintainable code for production ML applications.
• Experience building or operating production ML systems, including one or more of large-scale data and feature pipelines, distributed model training, low-latency inference, model deployment, or production monitoring.
• Ability to investigate ambiguous problems, reason from data, and choose pragmatic technical approaches.
• Ability to communicate technical ideas and results clearly and collaborate effectively across disciplines.

• Experience owning an ML problem across the full lifecycle, from problem framing and data exploration through deployment, monitoring, and iteration.
• Experience developing or productionizing modern deep learning, graph-learning, or LLM-based approaches.
• Experience building and optimizing real-time, low-latency ML systems at scale.
• Experience applying ML to fraud detection, integrity, trust and safety, identity, cybersecurity, or another adversarial domain.
• Experience rapidly building 0→1 products, working directly with customers, or operating effectively in a fast-moving environment.
• Active interest in the latest ML developments, and how they can be leveraged to solve business problems.