Growth Platform ML Engineer: Personalization & Recommendations

Fdetimes; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Stripe is seeking a machine learning engineer to design and deploy production ML systems that power product recommendations and personalized actions for businesses. You will work with data scientists, engineers, and product partners from problem definition through training, evaluation, deployment, monitoring, and iteration, turning model improvements into measurable outcomes.

We value practical tradeoffs, robust code, and collaboration across Stripe's Growth Platform to deliver scalable,

Qualifications

• 3+ years of industry experience in ML engineering, software engineering, or applied data science with hands-on production ML experience.
• Strong Python programming skills and ability to write maintainable, tested production code.
• Experience with ML frameworks such as PyTorch, TensorFlow, XGBoost, or scikit-learn.
• Data pipelines, SQL, and distributed processing with Spark/PySpark.
• Solid understanding of statistics, model evaluation, and experiments, awareness of data leakage.
• Experience deploying, monitoring, and debugging production ML systems; balance quality, latency, cost.
• Ability to turn business problems into technical approaches and collaborate with teams.

Responsibilities

• 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 context.
• Build agent-based recommendation capabilities using business context to identify relevant products with evaluations of usefulness.
• Develop data and feature pipelines for training and inference; improve data freshness and feature quality.
• Build reusable tooling for model evaluation, retraining, and safe rollout to accelerate improvements.
• Own the quality and operation of ML components: write production-grade code, monitor models, and troubleshoot.
• Design and analyze online experiments, linking offline evals to product adoption and user experience.
• Collaborate with product engineering to integrate models into delivery systems and with ML infra teams to improve training/serving.
• Work with product, marketing, and sales to identify problems where ML adds value.

Skills

Python programming

Production ML systems

Software engineering

ML model training and evaluation

SQL

Distributed data processing

Statistics and experimentation

Model deployment and monitoring

Cross-functional collaboration

Tools

PyTorch

TensorFlow

XGBoost

scikit-learn

Spark

PySpark

Job description

Stripe is seeking a machine learning engineer to design and deploy production ML systems that power product recommendations and personalized actions for businesses. You will work with data scientists, engineers, and product partners from problem definition through training, evaluation, deployment, monitoring, and iteration, turning model improvements into measurable outcomes.

We value practical tradeoffs, robust code, and collaboration across Stripe's Growth Platform to deliver scalable,