Machine Learning Engineer III

Peloton•New York, NY

1d•$141,400 - $190,700•Hybrid

About The Position

The Personalization team at Peloton is seeking a machine learning engineer to drive personalization and recommendations for members across multiple platforms. The role focuses on improving engagement and discovery of Peloton content by researching and applying AI and machine learning techniques for content and non-content recommendations. The engineer owns the end-to-end lifecycle of machine learning products, from data engineering and foundational infrastructure to scalable microservices and LLM-based solutions that serve users in real time. The role involves close collaboration with ML and software engineers, product managers, and product analysts to test ideas that increase member engagement, using detailed member-engagement data to help shape the future of connected fitness. Peloton provides expert instruction and fitness content through innovative hardware, software, and exclusive content for members working out at home, outdoors, while traveling, or at the gym. The company is headquartered in New York City and serves members across the US, UK, Canada, Germany, Australia, and Austria. The position is based at Peloton’s New York City headquarters. The stated base salary range is $141,400–$190,700 USD. Compensation depends on factors including experience and business objectives, and may vary if the job location changes. Total rewards also include annual equity awards, an Employee Stock Purchase Plan, and region-specific health and welfare benefits.

Requirements

A degree in a highly quantitative field such as Computer Science, Machine Learning, Operational Research, Statistics, or Mathematics.
At least 3 years of experience in one or more of recommender systems, natural language processing, or computer vision.
Strong understanding of software engineering fundamentals, including data structures and algorithms.
Experience writing code in Python, Java, Kotlin, Go, or C/C++, with documentation for reproducibility.
Experience with relational and non-relational databases such as Postgres, MySQL, Cassandra, or DynamoDB.
Ability to write and speak about technical concepts to business, technical, and lay audiences, and to give data-driven presentations.
Experience designing and deploying scalable, low-latency microservices for machine learning model serving.
Hands-on experience with modern MLOps, including automated evaluation pipelines and model monitoring.

Nice To Haves

MS or PhD in a highly quantitative field such as Computer Science, Machine Learning, Operational Research, Statistics, or Mathematics.
Comfort working with near-real-time machine learning applications.
A proven track record of working with product managers to launch machine-learning-based product features.

Responsibilities

Build and improve AI and machine learning pipelines that power Peloton recommendations.
Research and apply best-in-class machine learning techniques for recommender systems.
Evaluate, implement, and improve machine learning models.
Run A/B tests and experiments and analyze results in collaboration with product analysts.
Engineer, deploy, and monitor scalable microservices that serve high-concurrency machine learning inference endpoints.
Develop and scale evaluation pipelines to measure model performance and bias in production environments.
Design, implement, and maintain robust microservices for high-throughput machine learning inference endpoints.
Architect and manage the machine learning infrastructure needed for sophisticated LLM-based features and real-time personalization.
Collaborate with platform teams to use their tools and infrastructure to rapidly test ideas and deliver personalized experiences.