Stripe is hiring a Machine Learning Engineer, Radar in 8217 Risk Engineering — Seattle. The overview below is synthesized from the employer posting on stripe.com: factual requirements and scope are preserved, but prose is rewritten with editorial context. Verify details and apply via the employer link.
Who we are
About Stripe
Company context from the listing:
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.
About the team
Company context from the listing:
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.
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
Day-to-day scope for the Machine Learning Engineer, Radar as described in the posting:
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
Day-to-day scope for the Machine Learning Engineer, Radar as described in the posting:
- 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
Experience and skills the team lists as required:
The listing frames the Machine Learning Engineer, Radar at Stripe as a hands-on product role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.
Minimum requirements
Experience and skills the team lists as required:
- 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
Experience and skills the team lists as required:
- 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
Build a standout application
For the Machine Learning Engineer, Radar at Stripe, reviewers look for concise evidence over buzzwords. Mirror the language of the posting sparingly, quantify support or delivery outcomes, and show how you handled ambiguity, time-zone collaboration and user empathy. Keep your resume to impact, keep your cover note to one page, and link to artifacts — tickets resolved, docs shipped, dashboards owned — that prove you can operate in a fast-moving Web3 team. Prepare to discuss a time you turned a confusing user report into a clear fix and how you measure quality in support and operations.
Web3 hiring values reliability: on-time follow-through, clear writing, and a track record of improving runbooks and tooling. Treat the application as a work sample. For interviews, be ready to walk through how you prioritize across time zones, handle a difficult user, and decide when to escalate versus resolve directly. Show how you document decisions so the next teammate benefits.
In a distributed Web3 org, trust builds through written clarity. Use the cover note to demonstrate it.