Machine Learning Engineer, Radar
Remote • Seattle
Posted 3d ago
Job Location
Seattle
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, 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 ML problems from conception to production, analyzing new fraud and abuse patterns, experimenting with model architectures, designing evaluations, deploying models, and monitoring performance. Your work may include developing transformer architectures for payment data, building graph-powered intelligence, leveraging LLMs to detect emerging attacks, or rapidly prototyping new protections.
Responsibilities
- Build, train, evaluate, and deploy models to detect fraud and abuse.
- Investigate emerging fraud and abuse patterns and develop ML solutions.
- Design features, datasets, evaluation methods, and experiments for complex environments.
- Improve model architectures, training methods, and production systems.
- Monitor production behavior and adapt models to changing data and attack patterns.
- Balance model quality with user impact, latency, reliability, and operational complexity.
- Partner with cross-functional teams and users to develop Radar products.
- Contribute to reusable tools and AI-assisted workflows for ML system development.
Requirements
- 2+ years of experience developing, evaluating, and deploying ML models in production.
- Strong foundations in machine learning, data analysis, statistics, model evaluation, and experiment design.
- Strong programming and software-engineering skills with experience in production ML applications.
- Experience building or operating production ML systems (e.g., large-scale data pipelines, distributed training, low-latency inference, model deployment, production monitoring).
- Ability to investigate ambiguous problems, reason from data, and choose pragmatic technical approaches.
- Ability to communicate technical ideas clearly and collaborate effectively across disciplines.