The opportunity
Growth Platform builds the machine learning systems that help businesses discover and use the Stripe products that meet their needs. Our recommendations reach users across the Dashboard, email, onboarding, documentation, and AI agent interfaces.
What you'll do
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.
What they're looking for
- + 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.
- Experience with recommendation systems, ranking, personalization, or marketplace and advertising optimization.
