Machine Learning Engineer, Radar
Remote • Seattle, South San Francisco
Posted 18h ago
Job Location
Seattle, South San Francisco
Tech Stack
Remote Work Policy
Fully remote
Categories
Machine Learning Engineer
About the job
The Radar ML team builds the machine learning systems that power Stripe’s Radar fraud and abuse product suite. Our models make real-time decisions across a significant share of internet commerce, helping businesses stop fraud, increase authorization, and grow safely. By turning economy-scale datasets into compelling ML products, the team directly improves customer outcomes and drives meaningful revenue for Stripe. The team is growing and expanding into multiple new areas, including building abuse protections across the full customer life-cycle, adaptive and customizable fraud systems for enterprises, and risk intelligence for over 50 global payment methods. You will own major ML systems and initiatives from problem definition to production, finding where ML can make the biggest difference, designing the approach, building and shipping models and the systems around them, and continuously improving them as data, users, and attackers change. Your work may involve protecting businesses from abuse across the customer lifecycle, building adaptive and customizable fraud systems for Stripe's largest users, or extending risk intelligence to over 50 global payment methods. Across all these, you'll use modern approaches such as transformer models over payment data, graph intelligence across consumers, and LLMs to detect emerging attacks. You will have opportunities to ship 0→1 products and 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 end-to-end design and delivery of ML projects, from problem framing and opportunity sizing through deployment, monitoring, and iteration.
- Build, train, evaluate, and deploy models to detect fraud and abuse across Stripe’s global network, and own the production systems they run on.
- Investigate emerging fraud and abuse patterns, identify high-impact ML applications, and rapidly develop and deploy protections.
- Design features, datasets, and evaluation strategies for noisy, delayed, and adversarial environments, setting high standards for experimental rigor.
- Make informed architectural and modeling trade-offs considering model quality, user impact, latency, reliability, and operational cost.
- Serve as the primary expert for owned systems, leading incident response for model regressions, reducing operational overhead, and enabling others to build upon these systems.
- Collaborate with engineering, product, data science, risk experts, and key users to translate modeling advancements into differentiated Radar products.
- Develop reusable components, tools, and AI-assisted workflows to streamline team processes and address recurring problems.
- Mentor engineers, provide constructive design and code reviews, and facilitate rapid onboarding for new team members.
Requirements
- 5+ years of experience developing, evaluating, and deploying machine learning models in a production environment.
- Proven track record of leading ML projects from problem definition to production with measurable impact.
- Deep understanding of 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 large-scale data/feature pipelines, distributed model training, low-latency inference, model deployment, or production monitoring.
- Experience mentoring engineers or technically leading multi-contributor projects.
- Ability to investigate ambiguous problems, reason from data, and select pragmatic technical approaches.
- Clear communication of technical ideas and results, with effective cross-disciplinary collaboration.
- Experience applying ML to adversarial domains like fraud detection, integrity, trust and safety, identity, or cybersecurity.
- 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.