Machine Learning Engineer, Radar

Sourced from simplify.jobs

Join Stripe's Radar ML team to build machine learning systems that power fraud and abuse protection across a significant share of internet commerce. You will own ML problems end-to-end, from analyzing emerging fraud patterns and exploring large-scale datasets to deploying and monitoring production models. The role spans transformer architectures for payment data, graph-powered intelligence, and LLM-based detection of emerging attacks.

• Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe's global network
• Investigate emerging fraud and abuse patterns and rapidly develop effective ML solutions
• Design features, datasets, evaluation methods, and experiments for noisy, delayed, and adversarial environments
• Improve model architectures, training methods, and production systems for quality, coverage, and adaptability
• Monitor production behavior and iterate as data, customer needs, and attack patterns change
• Balance model quality with legitimate-user impact, latency, reliability, and operational complexity
• Partner with engineering, product, data science, and risk experts to ship differentiated Radar products
• Contribute to reusable tools and AI-assisted workflows for ML development and operations

• 2+ years of experience developing, evaluating, and deploying machine learning models in production
• Strong foundations in machine learning, data analysis, statistics, model evaluation, and experiment design
• Strong programming and software-engineering skills with tested, maintainable production code
• Experience building or operating production ML systems (data/feature pipelines, distributed training, low-latency inference, deployment, or monitoring)
• Ability to investigate ambiguous problems, reason from data, and choose pragmatic technical approaches
• Clear technical communication and cross-disciplinary collaboration

• Owning an ML problem across the full lifecycle from framing to deployment and iteration
• Experience with modern deep learning, graph-learning, or LLM-based approaches
• Building and optimizing real-time, low-latency ML systems at scale
• Applying ML to fraud detection, integrity, trust and safety, identity, or cybersecurity
• Rapidly building 0→1 products or working directly with customers
• Active interest in the latest ML developments