Machine Learning Engineer, Stripe

• 3+ years of industry experience in machine learning engineering, software engineering, or applied data science, with hands-on experience building and shipping ML models in production
• Strong programming skills in Python and experience writing maintainable, tested production code
• Practical experience designing, training, and evaluating ML models using frameworks such as PyTorch, TensorFlow, XGBoost, or scikit-learn
• Experience building data or feature pipelines, proficiency in SQL, and familiarity with distributed data processing tools such as Spark or PySpark
• A strong understanding of statistics, model evaluation, and experimentation, including the ability to recognize data leakage and distinguish offline model improvements from business impact
• Experience deploying, monitoring, and debugging production ML systems, and evaluating tradeoffs among model quality, reliability, latency, and cost
• Ability to turn an open-ended business problem into a technical approach and collaborate effectively with engineering, data science, product, and business partners

Desirable

• You will build and operate production ML systems that improve how Stripe recommends products, content, and next steps to businesses.
• You will own work from problem definition and feature development through training, evaluation, deployment, monitoring, and iteration.
• Working with data scientists, engineers, and product partners, you will turn model improvements into measurable user and business outcomes
• Design, train, evaluate, deploy, and maintain models for recommendation, ranking, and personalized action selection across Growth Platform surfaces
• Improve contextual bandit and policy-learning approaches, including exploration, reward design, and how recommendations adapt to user context and feedback
• Build agent-based recommendation capabilities that use business context to identify relevant products and integration options, with evaluations that test recommendation quality and usefulness
• Develop reliable data and feature pipelines for training and inference. Improve data freshness, feature quality, and consistency between training and production
• Build reusable tooling for model evaluation, retraining, and safe rollout so the team can test and ship improvements faster
• Own the quality and operation of the team's ML components: write tested production code, monitor models and pipelines, investigate failures, and improve reliability, latency, and cost
• Design and analyze online experiments with data science partners. Connect offline evaluation to product adoption and incremental impact, with guardrails for dismissals, unsubscribes, and user experience
• Partner with product engineering to integrate models into recommendation delivery systems, and with ML infrastructure teams to use and improve Stripe's shared training, feature, and serving capabilities
• Work with product, marketing, and sales partners to identify problems that shared ML capabilities can solve, and make practical choices about where modeling adds value