Staff Machine Learning Engineer, Home Surfaces
Spotify
Staff Machine Learning Engineer, Home Surfaces is a still-open role at Spotify on Jobrow. Location: Remote · New York, NY · Boston, MA. Sourced from the company's public Lever board. First seen 12 days ago; last confirmed 9 days ago. Apply on the employer ATS — Jobrow is not the employer.
- Pay
- —
- Workplace
- Remote
- Location
- New York, NY · Boston, MA
- First seen
- 12 days ago
- Last seen
- 9 days ago
- Board
- Lever
About this role
The Personalization team makes deciding what to play next easier and more enjoyable for every listener.
- Function: Machine learning
- Seniority: staff
Location & pay
Remote · New York, NY · Boston, MA No pay range was listed on the public board feed.
Source
Jobrow reads the public Lever board for Spotify. First seen 12 days ago; last seen 9 days ago. We are not the employer — apply only on the company ATS.
Posting
The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them. The Surfaces Podcasts team owns podcast recommendations across some of Spotify’s most visible surfaces, including Home and the Now Playing view. We build and improve the recommendation systems that help listeners discover their next favorite show or episode while helping creators reach the right audiences. As a Staff Machine Learning Engineer, you’ll help shape the future of personalized discovery and engagement at Spotify. You’ll work at the intersection of recommendation systems, large language models, and production-scale machine learning infrastructure to build experiences for listeners at global scale. You’ll take models from research to production, help set technical direction in ambiguous problem spaces, and solve complex personalization challenges across Spotify.
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