Machine Learning Engineer, Payment Intelligence

$192,000 - $288,000/Yr

Stripe - San Francisco, CA

Full-time - Mid Level

Remote - San Francisco, CA

Credit Intermediation and Related Activities

About the position

The Machine Learning Engineer for Payment Intelligence at Stripe will be responsible for the end-to-end lifecycle of applied machine learning model development and deployment. This role focuses on enhancing consumer-facing products like Radar, Adaptive Acceptance, and Identity by leveraging machine learning to optimize payment transactions, minimize costs, and reduce fraud. The engineer will collaborate with various teams to design, build, and operate ML-powered payment decisioning systems, ensuring high standards of code quality and system design.

Responsibilities

• 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 using 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.
• Build workflows to streamline the integration of new models and behaviors into Stripe's core payment flow.
• Collaborate 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.

Requirements

• Over 3+ years 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.

Nice-to-haves

• An advanced degree in a quantitative field (e.g. stats, physics, computer science).
• Proven track record of building and deploying machine learning systems that have effectively solved critical business problems.
• Experience in adversarial domains like Payments, Fraud, Trust, or Safety.
• Experience working in Python, Java, and/or Ruby codebases.
• Experience in software engineering in a production environment.

Benefits

• Equity
• Company bonus or sales commissions/bonuses
• 401(k) plan
• Medical, dental, and vision benefits
• Wellness stipends