Machine Learning Engineer, Growth Platform

Job summary

Responsibilities

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.

• 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…
• 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.

What they're looking for

• 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…
• 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…

Tech stack

• Python
• PyTorch
• TensorFlow
• scikit-learn
• SQL
• Spark