The opportunity
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.
What you'll do
Set the technical strategy and multi-quarter roadmap for lifecycle abuse ML,: with product and engineering leadership: which abuse vectors to take on, in what order, and with what approach.
Design a persistent, continuously updated consumer risk profile that combines: signals from signups, logins, payments, product usage, refunds, and disputes. Evolve it from explainable composite scores toward model-driven risk scores for each type of abuse.
Lead development of models for abuse vectors such as multi-accounting,: account takeover, bots, free-trial and promotion abuse, unauthorized resale, non-payment, refund abuse, and friendly fraud. Use graph, sequence, and LLM-based approaches over payment, account, and product-usage data.
Solve the label problem by designing ways to learn from sparse, delayed,: merchant-specific, and weak labels, including merchant-shared feedback, LLM-assisted labeling, and network-level signals.
Define how we measure success: evaluation frameworks, impact and ROI estimates, and operating-point guidance that help businesses trade off abuse losses against growth and conversion.
Turn detection into action by shaping the risk controls, interventions, and: recommendations businesses can turn on at each step of the customer journey.
What they're looking for
- + years of experience developing, evaluating, and deploying machine learning: models in a production environment.
- A track record of technically leading large, ambiguous ML initiatives from: problem definition to production, with measurable business impact.
- Deep expertise in machine learning, statistics, model evaluation, and: experiment design, including where labels are noisy, delayed, or scarce.
- Strong software-engineering skills, and you remain a hands-on contributor who: regularly writes tested, maintainable production code.
- Experience designing and operating production ML systems at scale, including: data and feature pipelines, real-time inference, deployment, and monitoring, and making architecture tradeoffs across them.
- Experience setting technical direction across multiple engineers or teams,: and mentoring engineers to grow their scope.
- Ability to explain complex tradeoffs clearly to engineers, product leaders,: and customers, and to build alignment across functions.
- Experience applying ML to fraud, abuse, integrity, trust and safety,: identity, or security, where adversaries actively adapt to your defenses.