Machine Learning Engineer, Radar

The shape of it

In the posting’s own words

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.

What it asks for · 4

• 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

Also a plus

• 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

What the job covers

• 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

Tools and skills named

Words the posting leans on

• fraud8×
• models8×
• products5×
• requirements4×
• building3×
• directly3×
• experience3×
• payments3×
• radar3×
• abuse2×
• build2×
• data2×
• deep learning2×
• deploying models2×
• design2×
• fraud detection2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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