Senior Machine Learning Engineer, Personalization, Music Understanding

spotify; New York (NY); On-site; USD 150,000 - 230,000; Full time

Job summary

Spotify is seeking a Senior Machine Learning Engineer for Personalization and Music Understanding in New York City. You will build features that guide what people listen to next, from Blend to Discover Weekly, leveraging advances in ML and music understanding to improve user experience for hundreds of millions of listeners.

Join a cross-disciplinary team that collaborates with product, design, user research, and data science to deliver high-impact personalization solutions in real time.

Qualifications

• The Personalization team enhances recommendations and user experience.
• Experience with machine learning for music understanding is a plus.
• Ability to build features used by hundreds of millions of listeners.
• Collaboration with product, design, and data science is essential.

Responsibilities

• Develop and improve personalization features for music recommendations.
• Work at the intersection of machine learning, music understanding, and user experience.
• Contribute to systems powering conversational playlist generation and adaptive controls.

Job description

Short Summary

Senior Machine Learning Engineer, Personalization, Music Understanding with spotify. 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…

Key Details

• Position / opportunity: Senior Machine Learning Engineer, Personalization, Music Understanding
• Organization: spotify
• Country / coverage: United States
• Location: New York, NY
• Work arrangement: On-site
• Opportunity type: Jobs
• Sector: Education
• Compensation: Not specified
• Duration: Not specified
• Start date: 2026-04-29
• Application deadline: Not specified

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. 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 Who You Are Where You'll Be Personalization Permanent

Responsibilities

• 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.

Requirements / Eligibility

• 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.