Machine Learning Engineer, Link
Stripe · New York City
About this role
**Machine Learning Engineer — Link (Stripe)** **About Stripe** 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 (Link Fraud and Auth)** Link is a digital wallet designed for fast and secure online payments. The Link Fraud and Auth team works to make Link the most trusted and highest-performing way to pay—protecting consumers and merchants from fraud, abuse, and financial loss while maximizing authorization rates for good users. The team spans consumer experiences, payment infrastructure, and ML-powered risk systems. **What you’ll do** Build and operate models and risk decisioning systems that protect Link while helping more legitimate payments succeed. You’ll work across the full ML lifecycle—from analyzing fraud patterns and identifying opportunities to building, deploying, monitoring, and improving models in production. You’ll use data to form hypotheses, make practical modeling choices, and define technical direction in partnership with Engineering, Product, and Data Science. **Responsibilities** - Build, train, evaluate, deploy, and own ML models that detect fraud and abuse across Link. - Use large-scale datasets to investigate emerging threats, develop hypotheses, and improve payment performance. - Develop pragmatic ML solutions (including tree-based models and other approaches suited to real-time risk decisioning). - Design data pipelines, features, evaluation methods, experiments, and monitoring systems for reliable production models. - Build and improve risk decisioning systems that integrate with other parts of Stripe’s payments stack. - Own ambiguous problems end-to-end: analysis → problem definition → technical design → implementation → launch → measurement → iteration. - Collaborate across Engineering, Product, Data Science, and Risk to turn model improvements into durable product outcomes. **Who you are** We’re looking for candidates who meet the minimum requirements. Preferred qualifications are a bonus. **Minimum requirements** - 6+ years of industry experience building and shipping ML models in production. - Strong Python skills; experience with SQL, Spark, and XGBoost (or similar tools). - Strong knowledge of production ML systems (pipelines, feature development, evaluation, deployment, monitoring, iteration). - Experience with large/complex datasets and applying data analysis, statistics, and experimentation fundamentals. - Ability to take an open-ended business problem, determine where ML can help, and own the solution through production. - Strong judgment balancing model performance, system complexity, latency, and business impact. - Strong collaboration skills and ability to work across teams. **Preferred qualifications** - Experience applying ML to fraud detection, risk modeling, payment authorization, identity, account security, or other adversarial domains. - Experience building real-time, low-latency ML/risk decisioning systems at scale. - Experience integrating models into production services and designing reliable systems around model outputs. - Experience with payments, fintech, digital wallets, or money movement. - Strong software engineering skills across the ML and product stack.
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