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Staff Machine Learning Engineer, Home Surfaces

Spotify·New York, NY·via lever
remotestaff5+ yrsFull timellmml
What the posting is about

Join Spotify's Personalization team to shape personalized discovery and engagement. Work on recommendation systems and large language models to create moment-based experiences. Drive technical direction and solve complex personalization challenges at a global scale.

Read out of the posting
Levelstaff
Experience asked5+ years
EmploymentFull time
LocationNew York, NY
RemoteYes
Visa sponsorshipNot stated
SalaryNot published, and most postings do not
Posted2026-09-10
Found vialever, direct from their system

We saw it 8 days after it went up.

The posting, as the company wrote it
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. Surfaces Moments is a ML team within the Personalization Mission focused on creating moment-based experiences across Spotify surfaces. The team owns and evolves the experiences that help listeners quickly connect with the content that matters most to them, including the Home Shortcuts experience and the underlying intelligence that powers it. By combining cutting-edge machine learning, recommendation systems, and product thinking, the team delivers highly relevant, personalized experiences to millions of listeners around the world. As a Staff Machine Learning Engineer, you will 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 that delight users and drive meaningful impact. This role is ideal for someone who enjoys taking models from research to production, driving technical direction in ambiguous problem spaces, and solving complex personalization challenges at global scale.

Copied from Spotify’s own board, not rewritten. Original ↗

Also open at Spotify

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