Machine Learning Engineer, Transaction Risk

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 Transaction Risk team builds the machine learning systems behind Stripe Radar, helping businesses prevent fraud and abuse while letting legitimate customers through. The team combines intelligence from across Stripe’s global network with real-time models and AI agents to protect businesses across the customer lifecycle, from signup and free trials to payments and ongoing usage, with a growing focus on emerging threats facing AI companies.

The team is expanding into multiple new areas, including abuse protections across the full customer lifecycle and solutions for emerging token fraud problems.

What You’ll Do

You will own major machine learning systems and initiatives from problem definition through production. You will identify where machine learning can make the biggest difference, design the approach, build and ship models and surrounding systems, and continuously improve them as data, users, and attackers change.

Your work may include protecting businesses from abuse across the customer lifecycle, building adaptive and customizable fraud systems for Stripe’s largest users, or extending risk intelligence to more than 50 global payment methods. You will use modern approaches such as transformer models over payment data, graph intelligence across consumers, and large language models that detect emerging attacks.

You will have opportunities to ship 0-to-1 products and improve mission-critical machine learning models operating at Stripe scale, often driving several efforts in parallel and setting the technical bar for the team.

Responsibilities

• Lead the design and delivery of machine learning projects end to end, from framing the problem and sizing the opportunity through deployment, monitoring, and iteration.
• Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe’s global network, and own the production systems they run on.
• Investigate emerging fraud and abuse patterns, determine where machine learning can have the most impact, and turn those insights into shipped protections.
• Design features, datasets, and evaluation strategies for noisy, delayed, and adversarial environments, and establish rigorous experimentation practices.
• Make architecture and modeling tradeoffs involving model quality, legitimate-user impact, latency, reliability, and operational cost.
• Serve as the subject-matter expert for owned systems, lead incident response for model regressions, reduce operational toil, and make systems easy for others to build on.
• Partner with engineering, product, data science, risk experts, and Stripe users to turn modeling improvements into differentiated Radar products.
• Turn recurring problems into reusable components, tools, and AI-assisted workflows that improve team velocity.
• Mentor engineers, provide design and code reviews, and help new teammates ramp up.

Requirements

You are an experienced machine learning engineer with deep modeling expertise and strong engineering judgment. You have owned machine learning systems in production, shipped multiple meaningful improvements, and helped make the people around you more effective. You should be comfortable leading through ambiguity and applying machine learning to production systems addressing new forms of fraud and abuse.

Minimum Requirements

• 5+ years of experience developing, evaluating, and deploying machine learning models in a production environment.
• A track record of leading machine learning projects from problem definition to production with measurable impact.
• Deep foundations in machine learning, statistics, model evaluation, and experiment design.
• Strong programming and software engineering skills, including experience writing tested, maintainable code for production machine learning applications.
• Experience designing and operating production machine learning systems, including several of the following: large-scale data and feature pipelines, distributed model training, low-latency inference, model deployment, or production monitoring.
• Experience mentoring engineers or technically leading projects with multiple contributors.
• Ability to investigate ambiguous problems, reason from data, and choose pragmatic technical approaches.
• Ability to communicate technical ideas and results clearly and collaborate effectively across disciplines.

Preferred Qualifications

• Experience applying machine learning to fraud detection, integrity, trust and safety, identity, cybersecurity, or another adversarial domain.
• Experience developing or productionizing modern deep learning, graph learning, or large-language-model approaches.
• Experience building and optimizing real-time, low-latency machine learning systems at scale.
• Experience owning the operational health of critical machine learning systems, including on-call, incident response, and migrations.
• Experience rapidly building 0-to-1 products or working directly with customers.
• Experience building AI-assisted tooling or agents that measurably increased team velocity.