Machine Learning Engineer III

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 r

Responsibilities

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:

• 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 $141,400 — $190,700 USD

ABOUT PELOTON:

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 www.onepeloton.com.

Peloton is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. Equal employment opportunity has been, and will continue to be, a fundamental principle at Peloton, where all team members, applicants, and other covered persons are considered on the basis of their personal capabilities and qualifications without discrimination because of race, color, religion, sex, age, national origin, disability, pregnancy, genetic information, military or veteran status, sexual orientation, gender identity or expression, marital and civil partnership/union status, alienage or citizenship status, creed, genetic predisposition or carrier status, unemployment status, familial status, domestic violence, sexual violence or stalking victim status, caregiver status, or any other protected characteristic as established by applicable law. This policy of equal employment opportunity applies to all practices and procedures relating to recruitment and hiring, compensation, benefits, termination, and all other terms and conditions of employment. 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.

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.