Staff Machine Learning Engineer, Radar

Job summary

Responsibilities

You will lead the machine learning strategy for abuse prevention across the full customer lifecycle. Abuse doesn't stop at the payment. Bad actors create fake and duplicate accounts, take over legitimate ones, farm free trials and promotions, resell access they obtained below cost, use services…

• 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…
• Design a persistent, continuously updated consumer risk profile that combines signals from signups, logins, payments, product usage, refunds, and disputes.
• 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…
• 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…
• 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…

What they're looking for

• 8+ 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…