Senior Machine Learning Engineer, Personalization, Muse
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 138d ago
The date the source published, not the day we noticed it (2026-04-29). Last seen at its source just now.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
United States
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $161k–210k/yr
Middle 50% of 741 listings that do state pay — Engineering · Senior · United States · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
lever employer's own board first seen 30d ago · last seen just now
The listing
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.
You’ll join a team working at the intersection of machine learning, music understanding, and user experience. We focus on generating music sessions powering experiences like systems that power conversational playlist generation to give users more adaptive and intuitive control over what they listen to.
This team collaborates closely with product, design, user research, and data science to build personalized, high-impact features used by hundreds of millions of listeners worldwide.
What You'll Do
- Design, build, evaluate, and ship LLM-based solutions that give users more adaptive control over their listening experience
- Work on prompted playlist experiences with a focus on music fulfillment and session generation
- Collaborate with cross-functional partners across user research, design, data science, product, and engineering
- Prototype new ML approaches and bring them into production at global scale
- Build and improve systems that connect artists and fans in personalized and meaningful ways
- Contribute to the development of scalable ML systems serving hundreds of millions of users
- Promote best practices in ML system design, testing, evaluation, and deployment across the organization
- Actively contribute to a strong community of machine learning practitioners at Spotify
Who You Are
- You are experienced in machine learning and enjoy solving complex real-world problems in collaborative environments
- You have a strong background in machine learning, natural language processing, and generative AI
- You are comfortable applying theory to build real-world, production-ready applications
- You have hands-on experience building and deploying end-to-end ML systems at scale
- You are familiar with LLM-based systems and techniques for improving them using human feedback such as reinforcement fine-tuning, DPO, or similar approaches
- You have experience designing modular ML architectures and writing technical specifications in partnership with product teams
- You are experienced with large-scale distributed data processing tools such as Apache Beam or Apache Spark
- You have worked with cloud platforms like GCP or AWS
Where You'll Be
- This role is based in New York
- We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.