The opportunity
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.
What you'll do
Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe’s global network.
Investigate emerging fraud and abuse patterns, identify where ML can help,: and rapidly develop effective solutions.
Design features, datasets, evaluation methods, and experiments for noisy,: delayed, and adversarial environments.
Improve model architectures, training methods, and production systems to: increase model quality, coverage, and adaptability.
Monitor production behavior and improve models as data, customer needs, and attack patterns change.
Balance model quality with legitimate-user impact, latency, reliability, and operational complexity.
What they're looking for
- + years of experience developing, evaluating, and deploying machine learning: models in a production environment.
- Strong foundations in machine learning, data analysis, statistics, model evaluation, and experiment design.
- Strong programming and software-engineering skills, with experience writing: tested, maintainable code for production ML applications.
- Experience building or operating production ML systems, including one or more: of large-scale data and feature pipelines, distributed model training, low-latency inference, model deployment, or production monitoring.
- 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.
- Experience owning an ML problem across the full lifecycle, from problem: framing and data exploration through deployment, monitoring, and iteration.
- Experience developing or productionizing modern deep learning, graph-learning, or LLM-based approaches.