Staff Machine Learning Engineer, Personalization
New York, NY OR Remote
The Personalization team at Peloton is looking for a Staff Machine Learning Engineer to drive personalization and recommendations for our highly engaged members across multiple platforms. Their main focus will be to optimize the engagement and discovery of Peloton content through research and application of AI and ML techniques for content recommendations. They will work closely with ML Engineers, Software Engineers, Product Managers and Product Analysts to test ideas that drive member engagement. They 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 ML pipelines that power Peloton’s content 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.
- Productionize, deploy and monitor machine learning models and services.
- 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.
- 4+ years of experience in Machine Learning
- Experience/Interest 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.
- MS/PhD in highly quantitative fields including Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.
- Comfortable working with near real-time ML applications.
- Proven track record of working with product managers to launch ML-based product features.
Peloton is the leading interactive fitness platform globally, with a passionate community of 7 million Members in the US, UK, Canada, Germany, and Australia. Peloton makes fitness entertaining, approachable, effective, and convenient, while fostering social connections that motivate its Members to commit to their fitness journeys. An innovator at the nexus of fitness, technology, and media, Peloton reinvented the fitness industry by developing a first-of-its-kind subscription platform that seamlessly combines the best equipment, proprietary networked software, world-class streaming digital fitness and wellness content, and best-in-class fitness experts and Instructors.
At Peloton, we motivate the world to live better. “Together We Go Far” means that we are greater than the sum of our parts, stronger collectively when each one of us is at our best. By combining hardware, software, content, retail, apparel, manufacturing, Member support, and so much more, we deliver an exhilarating fitness experience that unlocks our members' greatness. Join our team to unlock yours.
Peloton is an equal opportunity employer and committed to creating an inclusive environment for all of our applicants. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. If you would like to request any accommodations from application through to interview, please email: email@example.com
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