10 hours ago
Responsibilities
- Lead ML projects end to end, from problem framing and opportunity sizing through deployment, monitoring, and iteration.
- Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe’s global network.
- Investigate emerging fraud and abuse patterns and turn insights into shipped protections.
- Design features, datasets, and evaluation strategies for noisy, delayed, and adversarial environments.
- Make architecture and modeling tradeoffs involving model quality, legitimate-user impact, latency, reliability, and operational cost.
- Own production ML systems, including incident response for model regressions and reducing operational toil.
- Partner with engineering, product, data science, risk experts, and Stripe users to improve Radar products.
- Build reusable components, tools, and AI-assisted workflows.
- Mentor engineers and contribute design and code reviews.
Requirements
- 5+ years of experience developing, evaluating, and deploying machine learning models in a production environment.
- Track record of leading ML projects from problem definition to production with measurable impact.
- Deep foundations in machine learning, statistics, model evaluation, and experiment design.
- Strong programming and software-engineering skills with experience writing tested, maintainable production ML code.
- Experience designing and operating production ML systems, including areas such as large-scale data and feature pipelines, distributed model training, low-latency inference, model deployment, or production monitoring.
- Experience mentoring engineers or technically leading projects with multiple contributors.
- Ability to investigate ambiguous problems, reason from data, choose pragmatic technical approaches, communicate technical ideas clearly, and collaborate across disciplines.
- Preferred: experience with fraud detection, integrity, trust and safety, identity, cybersecurity, or another adversarial domain.
- Preferred: experience developing or productionizing deep learning, graph-learning, or LLM-based approaches.
- Preferred: experience building real-time, low-latency ML systems at scale and owning operational health, on-call, incident response, or migrations.
- Preferred: experience rapidly building 0→1 products, working directly with customers, or building AI-assisted tooling or agents that improve team velocity.
Categories
About Stripe
Stripe builds financial infrastructure for internet businesses, offering APIs and tools for online and in‑person payments, subscriptions, marketplaces/payouts, fraud prevention, identity, tax, issuing, and treasury. It monetizes through per‑transaction fees and SaaS pricing for advanced products. Founded in 2010 and headquartered in South San Francisco, Stripe is privately held and serves companies from startups to large enterprises worldwide.