The Machine Learning Engineer for Payment Intelligence at Stripe will focus on optimizing transaction processes using machine learning. This role is part of the Payment Intelligence ML Engineering team, which aims to maximize successful transactions while minimizing costs and fraud. The engineer will collaborate with various teams to design, build, and deploy ML-powered payment decisioning systems. This position offers a unique opportunity to contribute to critical consumer-facing products like Radar, Adaptive Acceptance, and Identity.

• Over 3 years of industry experience building machine learning applications in large scale distributed systems.
• 2 years of experience working within a team responsible for developing, managing, and optimizing ML models or ML infrastructure.
• Experience designing and training machine learning models to solve critical business problems.
• Experience performing analysis, including querying data, defining metrics, or slicing and dicing data to model performance and business metrics.

• Design and deploy new models using tools such as Spark, Presto, XGBoost, Tensorflow, and PyTorch.
• Iteratively improve verification and fraud models to protect millions of users from fraud.
• Envision and develop new models for fraud detection by working with large payment datasets.
• Propose new feature ideas and design real-time data pipelines to incorporate them into models.
• Integrate new signals into ML pipelines and derive new ML features.
• Collaborate and execute projects cross-functionally with data science, product management, infrastructure, and risk teams.
• Ensure engineering outcomes meet or exceed established standards of excellence in code quality, system design, and scalability.
• Mentor engineers earlier in their technical careers to help them grow.
• Propose and implement innovative product ideas to reduce costs and combat fraud at Stripe.