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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer, Radar - **Company:** Stripe, Inc. - **Location:** South Burlington, VT, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Code Review, Software Debugging, Machine Learning, Azure Machine Learning, Large Language Models, Deep Learning, Model Validation, Stripe, Real-time Inference, Production Code, Machine Learning Operations - **Published:** October 30, 2026 - **Apply:** https://www.careerbuilder.com/job-details/staff-machine-learning-engineer-radar-south-burlington-vt--d5a5f4e4-d1b4-46c3-af05-d953cdfcc9f4 ## About the Role Machine Learning, Startup, Revenue Growth, eCommerce, Machine Learning, Training Data Sets, Risk, Reseller Channel, Artificial Intelligence (AI), Application Programming Interface (API), * 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 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. Preferred qualifications * Experience applying ML to fraud, abuse, integrity, trust and safety, identity, or security, where adversaries actively adapt to your defenses. * Experience with entity resolution, graph learning, or behavioral and sequence modeling of users, accounts, or devices (for example, detecting multi-accounting, account takeover, or bots). * Experience productionizing modern deep learning or LLM-based approaches, including using LLMs for labeling, investigation, or detection. * Experience building 01 ML products and working directly with customers to define what to build. * Experience building shared ML platforms, representations, or foundation models used across multiple products or teams. * Experience building AI-assisted tooling or agents that measurably increased a team's velocity. ## Description * 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. * Stay deeply hands-on: write substantial production code, prototype quickly, and personally land the most technically difficult components, with the reliability and latency standards of systems in Stripe's critical path. * Break ambiguous problems into parallel workstreams, unblock other engineers, and raise the team's bar through design review, code review, and mentorship. * Partner directly with leading Stripe users, fraud experts, product, and adjacent Stripe teams to ground the work in real user pain and change course quickly based on what you learn. * Build AI-native tooling, agents, and workflows that let the team investigate attacks and build and operate models dramatically faster. Who you are You are a hands-on technical leader who has built ML systems that matter, shipped them to production, and led others in doing the same. You thrive in ambiguity. You can take an open-ended problem with messy data and incomplete labels, find the highest-leverage path, get to user impact quickly, and change course when the data says so. You make the engineers around you better. You're as comfortable talking with a customer about the economics of their abuse problem as you are debugging a feature pipeline. You're excited to use frontier AI both in the systems you build and in how you build them.