Machine Learning Engineer, Radar

Who We Are

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, and we have a staggering amount of work ahead.

That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About The Team

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 like defenses against AI token theft, free trial abuse, and programmatic attacks.

What You’Ll Do

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.

Responsibilities

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

Who You Are

We're looking for someone who meets the minimum

Requirements

to be considered for the role.

, you are encouraged to apply.

6+ years of industry 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

Preferred Qualifications

are a bonus, not a requirement.

Experience building and optimizing real-time, low-latency ML infrastructure at scale Strong software engineering skills and ability to design ML solutions through entire product stack Experience applying ML to fraud detection, risk modeling, or a closely related domain Experience designing ML products used by millions of users