Machine Learning Engineer, Radar

The Radar ML team builds the fraud detection models that protect Stripe's $1.9 trillion payment network from fraud. The team owns 10 real-time deep learning models that must constantly evolve to stay ahead of fraudsters. Each ML improvement translates directly into dollar impact for Stripe and its users.

The team's models also power the Radar product suite that tens of thousands of businesses use to screen payments and manage fraud. Radar is growing fast, and the team is actively building new products to protect against AI token theft, free trial abuse, and scripted attacks.

In this role, you will own ML work across the full lifecycle: researching new fraud patterns, building and deploying models, and sharing results directly with top Stripe customers. You will have opportunities to optimize Stripe’s most intensive ML models, and opportunities to ship 0-to-1 products from scratch.

Build, train, evaluate, and deploy ML models that detect fraud across Stripe’s global payments network
Research emerging fraud patterns like token theft and develop ML solutions to address them
Apply advances in deep learning to improve model quality and detection rates at scale
Co-build new fraud and abuse products directly with top users

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.
2+ years of experience training, evaluating, and deploying ML models in a production environment
Proficiency in Python and common data and ML frameworks like SQL, Spark, and PyTorch
Strong knowledge of production ML systems; and data analysis, statistics, and experiment design fundamentals
Active interest in the latest ML developments, and how they can be leveraged to solve business problems
Strong software engineering skills and ability to design ML solutions through entire product stack
Experience building and optimizing real-time, low-latency ML infrastructure at scale
Experience applying ML to fraud detection, integrity, trust and safety, or a closely related domain