Senior ML Engineer - Personalization & Recommender Systems

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

Benefits offered by this job

Medical, dental and vision insurance

401k plan

Stock Purchase Plan

Parental leave

Job summary

The Personalization team at Peloton is seeking a machine learning engineer to drive recommendations and discovery for our engaged members across platforms. You will own the end-to-end lifecycle of ML products—from data engineering and infrastructure to scalable microservices and real-time inference.

You will collaborate with ML engineers, software engineers, product managers and analysts to test ideas that boost member engagement, using cutting-edge ML methods on rich engagement data.

Qualifications

• Degree or higher in a highly quantitative field with strong mathematical foundations.
• Experience building ML-based recommender systems or NLP applications.
• Strong software engineering practices and reproducible research.
• Experience with deploying scalable ML systems and microservices.

Responsibilities

• Build and improve AI and ML pipelines powering Peloton recommendations.
• Research and apply 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 inference microservices.
• Develop evaluation pipelines for model performance and bias.

Skills

Python

Java

Kotlin

Go

C/C++

SQL

NoSQL

MLOps

LLM

Microservices

Recommender systems

NLP

Education

MS/PhD in quantitative fields

Bachelor's degree in CS/Math/OR

Tools

PostgreSQL

MySQL

Cassandra

DynamoDB

Job description

The Personalization team at Peloton is seeking a machine learning engineer to drive recommendations and discovery for our engaged members across platforms. You will own the end-to-end lifecycle of ML products—from data engineering and infrastructure to scalable microservices and real-time inference.

You will collaborate with ML engineers, software engineers, product managers and analysts to test ideas that boost member engagement, using cutting-edge ML methods on rich engagement data.