Machine Learning Engineer, Transaction Risk
Stripe · Seattle, New York City
About this role
**Machine Learning Engineer, Transaction Risk** **Who we are** Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet. **About the team** The Transaction Risk team builds the machine learning systems behind Stripe Radar, helping businesses prevent fraud and abuse while letting legitimate customers through. The team combines intelligence from across Stripe’s global network with real-time models and AI agents to protect businesses across the customer lifecycle (from signup and free trials to payments and ongoing usage), with a growing focus on emerging threats facing AI companies. **What you’ll do** You will own major ML systems and initiatives from problem definition to production. You’ll identify where ML can make the biggest difference, design the approach, build and ship models and the systems around them, and keep improving them as data, users, and attackers change. Your work may include: - Protecting businesses from abuse across the customer lifecycle - Building adaptive and customizable fraud systems for Stripe’s largest users - Extending risk intelligence to 50+ global payment methods You’ll use modern approaches such as transformer models over payment data, graph intelligence across consumers, and LLMs that detect emerging attacks. You’ll also have opportunities to ship 0→1 products and improve mission-critical ML models operating at Stripe scale—often driving multiple efforts in parallel and setting the technical bar for the team. **Responsibilities** - Lead the design and delivery of ML projects end to end (framing the problem, sizing the opportunity, deployment, monitoring, and iteration) - Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe’s global network; own the production systems they run on - Investigate emerging fraud and abuse patterns and quickly turn insights into shipped protections - Design features, datasets, and evaluation strategies for noisy, delayed, and adversarial environments; set the bar for experimental rigor - Make sound architecture and modeling tradeoffs across model quality, legitimate-user impact, latency, reliability, and operational cost - Serve as the go-to expert for the systems you own (lead incident response for model regressions, reduce operational toil, and make 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 and provide thoughtful design and code reviews; help new teammates ramp up quickly **Who you are** You’re an experienced ML engineer who combines deep modeling expertise with strong engineering judgment. You’ve owned ML systems in production, shipped meaningful improvements, and helped make the people around you more effective. You move fast without breaking things, lead through ambiguity, and are excited to work at the frontier of payments and AI—turning advances in machine learning into production systems that address new forms of fraud and abuse. **Minimum requirements** - 5+ years of experience developing, evaluating, and deploying m
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