Machine Learning Engineer, Radar

Job summary

Responsibilities

You will own major ML systems and initiatives from problem definition to production. You'll find 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.

• Lead the design and delivery of ML projects end to end, from framing the problem and sizing the opportunity through deployment, monitoring, and iteration.
• Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe’s global network, and own the production systems they run on.
• Investigate emerging fraud and abuse patterns, decide where ML can have the most impact, and quickly turn those insights into shipped protections.
• Design features, datasets, and evaluation strategies for noisy, delayed, and adversarial environments, and set the bar for experimental rigor.
• Make sound architecture and modeling tradeoffs between model quality, legitimate-user impact, latency, reliability, and operational cost.

What they're looking for

• 5+ years of experience developing, evaluating, and deploying machine learning models in a production environment.
• A 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 code for production ML applications.
• Experience designing and operating production ML systems, including several of: large-scale data and feature pipelines, distributed model training, low-latency inference, model…