ABOUT THE ROLE

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.

YOUR DAILY IMPACT AT PELOTON

Build and improve AI and ML pipelines that power Peloton’s 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 the results in collaboration with our 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 to host high-throughput ML inference endpoints

Architect and manage the ML infrastructure necessary to support sophisticated LLM-based features and real-time personalization

Collaborate and work closely with our platform teams to leverage their tools and infrastructure to rapidly iterate on ideas that drive delightful personalized experiences for millions of users

YOU BRING TO PELOTON

Degree in highly quantitative fields including Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.

3+ years of experience working in at least one of following ML disciplines: recommender systems, natural language processing or computer vision

Strong understanding of software engineering principles and fundamentals including data structures and algorithms

Experience writing code in Python, Java, Kotlin, Go, C/C++ with documentation for reproducibility

Experience with relational and non-relational databases such as Postgres, MySQL, Cassandra, or DynamoDB

Experience writing and speaking about technical concepts to business, technical, and lay audiences and giving data-driven presentations

Experience designing and deploying scalable, low-latency microservices for ML model serving

Hands-on experience with modern MLOps, including automated evaluation pipelines and model monitoring

MS/PhD in highly quantitative fields including Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc. preferred

Comfortable working with near real-time ML applications, preferred

Proven track record of working with product managers to launch ML-based product features, preferred

#LI-DD1

#LI-Hybrid

The base salary range represents the low and high end of the anticipated salary range for this position based at our New York City headquarters. The actual base salary offered for this position will depend on numerous factors including, without limitation, experience and business objectives and if the location for the job changes. Our base salary is just one component of Peloton’s competitive total rewards strategy that also includes annual equity awards and an Employee Stock Purchase Plan as well as other region-specific health and welfare benefits.

As an organization, one of our top priorities is to maintain the health and wellbeing for our employees and their family. To achieve this goal, we offer robust and comprehensive benefits including: