Machine Learning Engineer, Transaction Risk

Latitude; Seattle (WA); Hybrid; USD 192,000 - 318,000; Full time

Job summary

Stripe is seeking a Machine Learning lead to own major ML systems from problem definition to production and to strengthen protections against fraud across the global network. You will design features, build low-latency models, and mentor engineers while collaborating with risk, product, and engineering teams.

You will drive 0→1 innovations and scalable ML infrastructure to support Radar’s evolving threat landscape, ensuring reliability and impact at Stripe scale, with a strong focus on

Qualifications

• 5+ years of experience developing, evaluating, and deploying machine learning models in a production environment.
• A track record of leading ML 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, with experience writing tested, maintainable code for production ML applications.
• Experience designing and operating production ML systems, including data pipelines, distributed training, low-latency inference, deployment, or 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 across disciplines.

Responsibilities

• Lead the design and delivery of ML 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, decide where ML can have the most impact, and quickly turn those insights into shipped protections.
• Design features, datasets, and evaluation strategies for noisy, delayed, and adversarial environments, and set the bar for experimental rigor.
• Make sound architecture and modeling tradeoffs between model quality, legitimate-user impact, latency, reliability, and operational cost.
• Be the go-to expert for the systems you own: lead incident response for model regressions, reduce operational toil, and make your systems easy for others to build on.
• Partner with engineering, product, data science, risk experts, and leading Stripe users to turn modeling improvements into differentiated Radar products.
• Turn recurring problems into reusable components, tools, and AI-assisted workflows that speed up the whole team.
• Mentor engineers, give thoughtful design and code reviews, and help new teammates ramp up quickly.

Skills

ML production

Project leadership

Statistics

Software engineering

Low-latency inference

Mentoring

Cross-team communication

Job description

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

The Transaction Risk team builds the machine learning systems behind Stripe Radar, helping businesses prevent fraud and abuse while letting legitimate customers through. We combine 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 the emerging threats facing AI companies. The team is growing, and expanding into multiple new areas. We are building abuse protections across the full customer life-cycle and solving emerging token fraud problems to make Stripe the default fraud and abuse solution for AI.

What you’ll do

You will own major ML systems and initiatives from problem definition to production. You'll find where ML can make the biggest difference, design the approach, build and ship models and the systems around them, and keep improving 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 50+ global payment methods. Across all of these, you'll use modern approaches, such as transformer models over payment data, graph intelligence across consumers, and LLMs that detect emerging attacks.

You will have opportunities to ship 0→1 products, and to improve mission-critical ML 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 ML 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, decide where ML can have the most impact, and quickly turn those insights into shipped protections.
• Design features, datasets, and evaluation strategies for noisy, delayed, and adversarial environments, and set the bar for experimental rigor.
• Make sound architecture and modeling tradeoffs between model quality, legitimate-user impact, latency, reliability, and operational cost.
• Be the go-to expert for the systems you own: lead incident response for model regressions, reduce operational toil, and make your systems easy for others to build on.
• Partner with engineering, product, data science, risk experts, and leading Stripe users to turn modeling improvements into differentiated Radar products.
• Turn recurring problems into reusable components, tools, and AI-assisted workflows that speed up the whole team.
• Mentor engineers, give thoughtful design and code reviews, and help new teammates ramp up quickly.

Minimum Requirements

• 5+ years of experience developing, evaluating, and deploying machine learning models in a production environment.
• A track record of leading ML 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, with experience writing tested, maintainable code for production ML applications.
• Experience designing and operating production ML systems, including several of: 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 ML to fraud detection, integrity, trust and safety, identity, cybersecurity, or another adversarial domain.
• Experience developing or productionizing modern deep learning, graph-learning, or LLM-based approaches.
• Experience building and optimizing real-time, low-latency ML systems at scale.
• Experience owning the operational health of critical ML systems, including on-call, incident response, and migrations.
• Experience rapidly building 0→1 products or working directly with customers.
• Experience building AI-assisted tooling or agents that measurably increased a team's velocity.

Hybrid work at Stripe

This role is available either in an office or a remote location (35+ miles or 56+ km from a Stripe office).

In-office expectations

Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams.

Working remotely at Stripe

A remote location is defined as being 35 miles (56 kilometers) or more from one of our offices. While you would be welcome to come into the office for team/business meetings, on-sites, meet-ups, and events, our expectation is you would regularly work from home rather than a Stripe office. Stripe does not cover the cost of relocating to a remote location. We encourage you to apply for roles that match the location where you currently live or plan to live.

Pay and benefits

The annual US base salary range for this role is $192,000 - $318,000. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. This salary range may be inclusive of several career levels at Stripe and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location. Applicants interested in this role and who are not located in the US may request the annual salary range for their location during the interview process.

Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends.

We look forward to hearing from you.

At Stripe, we’re looking for people with passion, grit, and integrity. You’re encouraged to apply even if your experience doesn’t precisely match the job description. Your skills and passion will stand out—and set you apart—especially if your career has taken some extraordinary twists and turns. At Stripe, we welcome diverse perspectives and people who think rigorously and aren’t afraid to challenge assumptions. Join us.