Senior ML Engineer: Personalization & Recs

Peloton Interactive; New York (NY); On-site; USD 141,000 - 191,000; Full time

Benefits offered by this job

Health insurance

Equity awards

Employee Stock Purchase Plan

Job summary

Peloton seeks a machine learning engineer to advance recommendations across platforms, owning end-to-end ML products from data engineering to scalable microservices and real-time inference. You will test ideas with ML engineers, software engineers, PMs and analysts to boost member engagement.

Ideal candidates have 3+ years in ML disciplines (recommender systems, NLP or CV), strong software fundamentals, and experience building deployable ML services.

Qualifications

• Degree in highly quantitative fields such as CS, ML, OR, Stats, Math.
• 3+ years in ML disciplines: recommender systems, NLP or CV.
• Strong software engineering fundamentals and data structures.
• Experience in Python, Java, Kotlin, Go or C/C++, with reproducible docs.
• Experience deploying scalable, low-latency ML microservices.
• Hands-on ML ops: automated evaluation pipelines and monitoring.

Responsibilities

• Build and improve ML pipelines powering Peloton recommendations.
• Research and apply state-of-the-art ML techniques for recommender systems.
• Evaluate, implement, and improve ML models in production.
• Run A/B tests and analyze results with product analysts.
• Engineer, deploy, and monitor scalable ML microservices.
• Scale evaluation pipelines for model performance and bias.
• Architect ML infrastructure for real-time personalization and LLM features.
• Collaborate with platform teams to iterate on ideas.

Skills

ML engineering

Recommender systems

NLP / CV

Python / Java / Go

A/B testing

Technical communication

Education

MS/PhD preferred

BS in quantitative field

Tools

Postgres

MySQL

Cassandra

DynamoDB

Kubernetes

Job description

Peloton seeks a machine learning engineer to advance recommendations across platforms, owning end-to-end ML products from data engineering to scalable microservices and real-time inference. You will test ideas with ML engineers, software engineers, PMs and analysts to boost member engagement.

Ideal candidates have 3+ years in ML disciplines (recommender systems, NLP or CV), strong software fundamentals, and experience building deployable ML services.