Machine Learning Engineer III at Peloton

Company: Peloton
Location: New York, New York
Employment type: full-time
Posted: 2026-09-01

ABOUT THE ROLE &nbsp; The Personalization team at Peloton is looking for a machine learning engineer to drive personalization and recommendations for our highly engaged members across multiple platforms. Your main focus will be to optimize the engagement and discovery of Peloton content through research and application of AI and ML techniques for content and non-content recommendations. You will own the end-to-end lifecycle of our ML products, from data engineering and foundational infrastructure to building scalable microservices and LLM-based solutions that serve our users in real-time. You will work closely with ML Engineers, Software Engineers, Product Managers and Product Analysts to test ideas that drive member engagement. You will have a unique opportunity to work with one of the most granular data related to member engagement in the fitness industry. We’re looking for someone who’s passionate about fitness and is excited about the challenges of AI and machine learning to define the future of connected fitness. &nbsp; YOUR DAILY IMPACT AT PELOTON &nbsp; <ul style="margin-top: 0px; margin-bottom: 0px;"> <li style="font-size: 11pt; font-family: 'Proxima Nova', sans-serif;"> Build and improve AI and ML pipelines that power Peloton’s recommendations </li> <li style="font-size: 11pt; font-family: 'Proxima Nova', sans-serif;"> Research and apply best-in-class machine learning techniques for recommender systems </li> <li style="font-size: 11pt; font-family: 'Proxima Nova', sans-serif;"> Evaluate, implement, and improve machine learning models </li> <li style="font-size: 11pt; font-family: 'Proxima Nova', sans-serif;"> Run A/B tests and experiments and analyze the results in collaboration with our product analysts </li> <li style="font-size: 11pt; font-family: Arial, sans-serif;"> Engineer, deploy, and monitor scalable microservices that serve high-concurrency machine learning inference endpoints </li> <li style="font-size: 11pt; font-family: Arial, sans-serif;"> Develop and scale evaluation pipelines to measure model performance and bias in production environments </li> <li style="font-size: 11pt; font-family: Arial, sans-serif;"> Design, implement, and maintain robust microservices to host high-throughput ML inference endpoints </li> <li style="font-size: 11pt; font-family: Arial, sans-serif;"> <span style="font-

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