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.
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
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
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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:
Base Salary Range
$141,400—$190,700 USD
Peloton (NASDAQ: PTON) provides Members with expert instruction, and world class content to create impactful and entertaining workout experiences for anyone, anywhere and at any stage in their fitness journey. At home, outdoors, traveling, or at the gym, Peloton brings together innovative hardware, distinctive software, and exclusive content. Founded in 2012 and headquartered in New York City, Peloton has millions of Members across the US, UK, Canada, Germany, Australia, and Austria. For more information, visit
If you would like to request any accommodations from application through to interview, please email: applicantaccommodations@onepeloton.com.
At Peloton, we embrace technology, including AI, to enhance productivity and accelerate innovation in the work we do for our members. However, in our hiring process, our priority remains in getting to know you and your unique qualifications. To ensure a fair and equitable process, we do not permit the use of AI tools during any stage of the application and interview process. In considering you as an applicant, we want to understand your skills, experiences, and motivations without mediation through an AI system. We also want to directly assess your communication skills without the use of an AI tool.
Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance and the San Francisco Fair Chance Ordinance, as applicable to applicants applying for positions in these jurisdictions.
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