Machine Learning Engineer, Link
Remote • New York City
Posted 10d ago
Remote Work Policy
Fully remote
Categories
Machine Learning Engineer
About the job
Stripe is seeking a Machine Learning Engineer for its Link Fraud and Auth team. This role involves building and operating models and risk decisioning systems to protect Link while enabling legitimate payments. You will manage the full machine learning lifecycle, from analyzing fraud patterns and identifying opportunities to developing, deploying, monitoring, and improving models in production. Your work will directly impact Link's fraud performance, authorization rates, and expansion into new products and payment experiences, contributing to Stripe's mission to increase the GDP of the internet.
Responsibilities
- Build, train, evaluate, deploy, and own machine learning models for fraud and abuse detection across Link.
- Investigate emerging threats and identify opportunities to improve payment performance using large-scale datasets.
- Develop pragmatic machine learning solutions, including tree-based models, for real-time risk decisioning.
- Design data pipelines, features, evaluation methods, experiments, and monitoring systems for reliable production models.
- Build and improve risk decisioning systems integrated with Stripe's payments stack.
- Own ambiguous problems from analysis and problem definition through to production launch and iteration.
- Collaborate with Engineering, Product, Data Science, and Risk partners to translate model improvements into product outcomes.
Requirements
- 6+ years of industry experience building and shipping machine learning models in production.
- Strong programming skills in Python and experience with data/ML tools like SQL, Spark, and XGBoost.
- Strong knowledge of production machine learning systems (data pipelines, feature development, model evaluation, deployment, monitoring, iteration).
- Experience with large, complex datasets and applying data analysis, statistics, and experimentation fundamentals.
- Demonstrated ability to solve open-ended business problems with machine learning solutions and own them through production.
- Strong judgment in selecting practical modeling approaches and evaluating tradeoffs (performance, complexity, latency, business impact).
- Strong collaboration skills and ability to work across teams.