stripe
Machine Learning Engineer, Growth Platform
About this role
Stripe seeks a Machine Learning Engineer to design and operate production ML systems for their Growth Platform, which delivers personalized product recommendations to millions of businesses. You'll own the full lifecycle of recommendation and ranking models, from problem definition through deployment and monitoring, while collaborating with data scientists, engineers, and product teams to drive measurable business outcomes.
What you'll do
- Design, train, evaluate, deploy, and maintain recommendation, ranking, and personalization models across multiple Stripe surfaces
- Improve contextual bandit and policy-learning approaches, including exploration strategies and reward design
- Develop reliable data pipelines and feature systems for training and inference with strong freshness and consistency
- Build reusable ML tooling for evaluation, retraining, and safe model rollout to accelerate team velocity
- Monitor, debug, and optimize production ML systems for reliability, latency, cost, and model quality
- Design and analyze online experiments with data science partners to connect offline improvements to business impact
What they're looking for
- Python and production-quality code development
- ML frameworks (PyTorch, TensorFlow, XGBoost, scikit-learn)
- SQL and distributed data processing (Spark, PySpark)
- Statistical modeling, evaluation, and experimentation design
- Recommendation systems and ranking algorithms
- Contextual bandits and policy learning
- Production ML deployment and monitoring
- LLM applications and embeddings
Opens the official application on the employer’s site. No login required.
stripe
Stripe builds payment infrastructure and financial services platforms, offering APIs and tools that enable developers and businesses to process transactions, detect fraud, verify identity, and manage security at scale. The company is hiring Backend Engineers, Full Stack Engineers, ML Engineers, AI Engineers, and Security Engineers to develop core platform systems, payment intelligence, customer support infrastructure, and security data platforms.
- Website
- stripe.com
Likely interview questions
- Walk us through a production ML system you built end-to-end, from problem definition to measurement—what were the key tradeoffs?
- How do you approach designing and evaluating a contextual bandit system for recommendations, and how would you handle exploration-exploitation tradeoffs?