Machine Learning Engineer, Radar

at Stripe

📍 New York City, United States
📍 South San Francisco, United States
📍 Seattle, United States

USD 180,000-270,000 per year

MIDDLE

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI Data Analysis @ 6 Deep Learning Fraud @ 3 Machine Learning @ 3 Payments PyTorch @ 5 Python @ 5 SQL @ 5 Spark @ 5 Statistics @ 6

Details

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. Stripe’s mission is to increase the GDP of the internet.

About the Team

The Radar ML team builds 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, which tens of thousands of businesses use to screen payments and manage fraud. Radar is growing fast, and the team is building new products to protect against AI token theft, free trial abuse, and scripted attacks.

Responsibilities

Build, train, evaluate, and deploy machine learning models that detect fraud across Stripe’s global payments network.
Research emerging fraud patterns, such as token theft, and develop machine learning 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.
Own machine learning work across the full lifecycle, including researching new fraud patterns, building and deploying models, and sharing results directly with top Stripe customers.
Optimize Stripe’s most intensive machine learning models and ship new products from scratch.

Requirements

Minimum Requirements

At least 2 years of experience training, evaluating, and deploying machine learning models in a production environment.
Proficiency in Python and common data and machine learning frameworks, including SQL, Spark, and PyTorch.
Strong knowledge of production machine learning systems, data analysis, statistics, and experiment design fundamentals.
Active interest in the latest machine learning developments and how they can be leveraged to solve business problems.

Preferred Qualifications

Strong software engineering skills and the ability to design machine learning solutions across the entire product stack.
Experience building and optimizing real-time, low-latency machine learning infrastructure at scale.
Experience applying machine learning to fraud detection, integrity, trust and safety, or a closely related domain.