Job details

Machine Learning Engineer

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. 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.

YOU BRING TO PELOTON

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.
Produce, 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.

YOU BRING TO PELOTON

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

BONUS

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.

#LI-JS2

#LI-Hybrid

The base salary range represents the low and high end of the anticipated salary range for this position based at either our New York City headquarters or Santa Clara office. The actual base salary offered for this position will depend on numerous factors including individual performance, 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:

Medical, dental and vision insurance
Generous paid time off policy
Short-term and long-term disability
Access to mental health services
401k, tuition reimbursement and student loan paydown plans
Employee Stock Purchase Plan
Fertility and adoption support and up to 18 weeks of paid parental leave
Child care and family care discounts
Free access to Peloton Digital App and apparel and product discounts
Commuter benefits and Citi Bike Discount
Pet insurance and so much more!

Base Salary Range

$171,600—$223,000 USD

ABOUT PELOTON:

Peloton provides Members with expert instruction, world-class content and the fitness industry's leading music library 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 offers an immersive and personalized experience [with or without equipment]. Access Peloton content via the Peloton Bike, Bike+, Tread, Guide, Row or the Peloton App, now with multiple membership tiers. Founded in 2012 and headquartered in New York City, Peloton has a highly engaged community of nearly 7 million Members across the US, UK, Canada, Germany, and Australia.

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: applicantaccommodations@onepeloton.com

Please be aware that fictitious job openings, consulting engagements, solicitations, or employment offers may be circulated on the Internet in an attempt to obtain privileged information, or to induce you to pay a fee for services related to recruitment or training. Peloton does NOT charge any application, processing, or training fee at any stage of the recruitment or hiring process. All genuine job openings will be posted here on our careers page and all communications from the Peloton recruiting team and/or hiring managers will be from an @onepeloton.com email address.

If you have any doubts about the authenticity of an email, letter or telephone communication purportedly from, for, or on behalf of Peloton, please email applicantaccommodations@onepeloton.com before taking any further action in relation to the correspondence.

Peloton does not accept unsolicited agency resumes. Agencies should not forward resumes to our jobs alias, Peloton employees or any other organization location. Peloton is not responsible for any agency fees related to unsolicited resumes.