Radar ML Engineer - Real-Time Fraud & Abuse Defender

Stripe, Inc.; San Francisco (CA); On-site; USD 212,000 - 318,000; Full time

Job summary

Stripe, Inc. in San Francisco is seeking an experienced Machine Learning Engineer to own major ML systems from problem framing to production, enabling Radar fraud and abuse protections at scale.

You will design, train, evaluate, and deploy models, mentor engineers, lead incident responses, and collaborate with product and risk teams to turn modeling advances into reliable protections for Stripe’s global network.

Qualifications

• 5+ years of experience developing, evaluating, and deploying machine learning models in a production environment.
• A track record of leading ML projects from problem definition to production, with measurable impact.
• Deep foundations in machine learning, statistics, model evaluation, and experiment design.
• Strong programming and software‑engineering skills, with experience writing tested, maintainable code for production ML applications.
• Experience designing and operating production ML systems, including several of: large‑scale data and feature pipelines, distributed model training, low‑latency inference, model deployment, or production monitoring.
• Experience mentoring engineers or technically leading projects with multiple contributors.
• 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.

Responsibilities

• Lead the design and delivery of ML projects end to end, from framing the problem and sizing the opportunity through deployment, monitoring, and iteration.
• Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe’s global network, and own the production systems they run on.
• Investigate emerging fraud and abuse patterns, decide where ML can have the most impact, and quickly turn those insights into shipped protections.
• Design features, datasets, and evaluation strategies for noisy, delayed, and adversarial environments, and set the bar for experimental rigor.
• Make sound architecture and modeling tradeoffs between model quality, legitimate-user impact, latency, reliability, and operational cost.
• Be the go‑to expert for the systems you own: lead incident response for model regressions, reduce operational toil, and make your systems easy for others to build on.
• Partner with engineering, product, data science, risk experts, and leading Stripe users to turn modeling improvements into differentiated Radar products.
• Turn recurring problems into reusable components, tools, and AI‑assisted workflows that speed up the whole team.
• Mentor engineers, give thoughtful design and code reviews, and help new teammates ramp up quickly.

Skills

ML modeling

Production ML systems

Engineering judgment

Mentoring engineers

Problem solving

Cross-functional collaboration

Experiment design

Communication

Job description

Stripe, Inc. in San Francisco is seeking an experienced Machine Learning Engineer to own major ML systems from problem framing to production, enabling Radar fraud and abuse protections at scale.

You will design, train, evaluate, and deploy models, mentor engineers, lead incident responses, and collaborate with product and risk teams to turn modeling advances into reliable protections for Stripe’s global network.