Machine Learning Engineer, Radar

Job summary

Responsibilities

You will own ML problems from conception to production: analyzing new fraud and abuse patterns, exploring large-scale datasets, experimenting with model architectures, designing evaluations, deploying models, and monitoring performance.

• 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.

What they're looking for

• 2+ 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…
• Ability to investigate ambiguous problems, reason from data, and choose pragmatic technical approaches.