Machine Learning Engineer, Radar

Stripe • South San Francisco, CA

22h

About The Position

Stripe’s Radar ML team builds machine learning systems for Radar’s fraud and abuse products. Its models make real-time decisions across a significant share of internet commerce, helping businesses stop fraud, increase authorization, and grow safely. The team is expanding protections across the customer lifecycle, adaptive and customizable fraud systems for enterprises, and risk intelligence for more than 50 global payment methods.

The Machine Learning Engineer owns major machine learning systems and initiatives from problem definition through production. The role identifies high-impact opportunities, designs and ships models and supporting systems, and improves them as data, users, and attackers change. Work may include applying transformer models to payment data, graph intelligence across consumers, or large language models to detect emerging attacks. The engineer may ship new products and improve mission-critical models at Stripe scale, often leading several efforts in parallel and setting the team’s technical bar.

Requirements

• At least 5 years of experience developing, evaluating, and deploying machine learning models in a production environment.
• Track record of leading machine learning 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, including writing tested, maintainable code for production machine learning applications.
• Experience designing and operating production machine learning 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.

Nice To Haves

• Experience applying machine learning to fraud detection, integrity, trust and safety, identity, cybersecurity, or another adversarial domain.
• Experience developing or productionizing modern deep learning, graph-learning, or LLM-based approaches.
• Experience building and optimizing real-time, low-latency machine learning systems at scale.
• Experience owning the operational health of critical machine learning systems, including on-call, incident response, and migrations.
• Experience rapidly building 0→1 products or working directly with customers.
• Experience building AI-assisted tooling or agents that measurably increased a team’s velocity.

Responsibilities

• Lead the design and delivery of machine learning projects end to end, from framing problems and sizing opportunities 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, identify where machine learning can have the most impact, and turn those insights into shipped protections.
• Design features, datasets, and evaluation strategies for noisy, delayed, and adversarial environments, and set standards for experimental rigor.
• Make architecture and modeling tradeoffs involving model quality, legitimate-user impact, latency, reliability, and operational cost.
• Serve as the expert for owned systems, lead incident response for model regressions, reduce operational toil, and make systems easier for others to build on.
• Partner with engineering, product, data science, risk experts, and Stripe users to turn modeling improvements into Radar products.
• Turn recurring problems into reusable components, tools, and AI-assisted workflows that improve team productivity.
• Mentor engineers, provide design and code reviews, and help new teammates ramp up.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed