Senior Machine Learning Engineer, Personalization, Magenta
Spotify · Remote
MeritLog read this listing from Spotify's Lever job board and last checked it on September 10, 2026.
Source: the employer's Lever job board. Open the original listing for current details.
Job details
- Work model
- Remote
- Salary
- Not listed by source
- Location
- New York, NY
- Occupation
- Computer Systems Engineers/Architects(O*NET 15-1299.08)
- Company website
- newsroom.spotify.com
Hiring context
How this role compares at Spotify
Spotify has 79 live roles in MeritLog’s catalog across 12 job families, and 27 of them are in data & analytics. 0 of those listings publish a pay range, a disclosure rate of 0%.
Spotify concentrates this hiring in:
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
Job description
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 Sessions Department within Personalization is building a portfolio of agentic and conversational products that define how hundreds of millions of people discover and experience audio, such as prompted Playlists or DJ, all powered by a single layer that understands music, culture, and the user’s taste You'll join a team of four engineers actively building the agent and the strategy behind it. We work closely with the broader Sessions organization on one of the most highly-leveraged bets at Spotify right now: making it possible to have natural language conversation with Spotify across the entire app! The team moves fast by staying hyper-focused: we pick a focused set of problems, ship new features to users weekly, and learn in the wild. We constantly dogfood our product and learn from users' data and feedback to find the most important next thing to build or improve, together.
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