The Personalization team in the AI/ML organization at Peloton is looking for a Staff Applied Scientist to help us envision and build the future of personalization at Peloton. With access to the largest collection of fitness data, your main focus will be to work closely with other ML Engineers, data engineers, and product analysts to improve our Machine Learning models and infrastructure for Peloton across our numerous platforms. The role would focus on driving recommender systems’ innovations in our personalization team’s unique domain of multi-modal fitness data from numerous platforms to guide our members in their fitness journey. You’ll be able to spot gaps in how we’ve done things before, and you’ll be empowered to find better ways to do them. We embrace the culture of experimentation, so you will have the opportunity to rapidly test out your ideas and measure their impact.
In this role, you will:
- Develop high impact recommender models, owning all stages including ideation, development, deployment, A/B testing, and monitoring with support from our existing machine learning and AI platform teams.
- Execute A/B tests as needed to evaluate the performance of ML models against the status quo.
- Work closely with product managers and product analysts to understand new opportunities for the personalization team, and develop detailed requirements and design end-to-end solutions at scale.
- Conduct cutting-edge research in recommendation related problems, and apply the technology to different business problems.
- Inform and influence the development of better infrastructure for experimentation and deployment of ML models.
- Represent Peloton at top tier conferences and meetup groups.
- 4+ Years of experience developing and deploying machine learning algorithms at scale.
- Familiar with common recommender systems algorithms such as Matrix Factorization, Bayesian Personalized Ranking, GRU4Rec, DLRM etc.
- Strong programming background, with extensive experience in Python.
- Familiar with the underlying frameworks and implementation mechanisms of at least one of the mainstream deep learning frameworks, like TensorFlow, PyTorch or MXNet
- Applied knowledge of A/B testing, self-sufficient to evaluate your machine learning models
- Entrepreneurial and self-directed, innovative, biased towards action in fast-paced environments.
- Able to take complete ownership of a feature or project.
- Experience working with one of the cloud providers preferably AWS.
- Experience working in a shared code repository with a version control system (Git / SVN)
- Publications in top tier machine learning conferences such as ICML, NeurIPS, ICLR, RecSys and KDD.
- Familiarity with experiment and model tracking systems such as Comet, MLFlow or Metaflow.
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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