Machine Learning Engineer, Growth Platform
Remote • San Francisco
Posted 10d ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Categories
Machine Learning Engineer
About the job
Growth Platform at Stripe builds machine learning systems to help businesses discover and utilize Stripe products. These recommendations are integrated across various platforms including the Dashboard, email, onboarding, documentation, and AI agent interfaces. The team combines business understanding with models to determine the most useful recommendations, optimal timing, and effective learning from outcomes. Their work encompasses recommendation and ranking models, contextual bandits, agent-based recommendations, and the underlying data and evaluation systems, aiming to drive business growth through relevant product adoption while maintaining a positive user experience.
Responsibilities
- Design, train, evaluate, deploy, and maintain models for recommendation, ranking, and personalized action selection.
- Improve contextual bandit and policy-learning approaches, including exploration, reward design, and adaptive recommendations.
- Build agent-based recommendation capabilities using business context to identify relevant products and integration options.
- Develop reliable data and feature pipelines for training and inference, focusing on freshness, quality, and consistency.
- Build reusable tooling for model evaluation, retraining, and safe rollout to accelerate improvements.
- Own the quality and operation of ML components, including writing tested production code, monitoring, and improving reliability, latency, and cost.
- Design and analyze online experiments, connecting offline evaluation to product adoption and business impact.
- Partner with product engineering to integrate models into delivery systems and ML infrastructure teams to leverage and enhance shared capabilities.
- Collaborate with product, marketing, and sales partners to identify problems solvable by shared ML capabilities and determine where modeling adds value.
Requirements
- 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 like PyTorch, TensorFlow, XGBoost, or scikit-learn.
- Experience building data or feature pipelines, proficiency in SQL, and familiarity with distributed data processing tools like Spark or PySpark.
- Strong understanding of statistics, model evaluation, and experimentation, including recognizing data leakage and distinguishing offline model improvements from business impact.
- Experience deploying, monitoring, and debugging production ML systems, evaluating tradeoffs among model quality, reliability, latency, and cost.
- Ability to translate open-ended business problems into technical approaches and collaborate effectively with cross-functional partners.