The Personalization team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people use the products we build, including destinations like Home and Search, original playlists like Discover Weekly and Daylist, and new innovations like AI DJ and AI Playlists.

The Surfaces Music team is responsible for music recommendations across Spotify's most visible surfaces, including Home and the Now Playing experience. We own music shelf and candidate generation as well as the ranking models that power these experiences. Our models include embedding models for deep catalog discovery, new release recommendations, and a unified transformer-based generative personalization model that is poised to reshape how we deliver personalized experiences across Spotify.

The Personalization team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people use the products we build, including destinations like Home and Search, original playlists like Discover Weekly and Daylist, and new innovations like AI DJ and AI Playlists.

The Surfaces Music team is responsible for music recommendations across Spotify's most visible surfaces, including Home and the Now Playing experience. We own music shelf and candidate generation as well as the ranking models that power these experiences. Our models include embedding models for deep catalog discovery, new release recommendations, and a unified transformer-based generative personalization model that is poised to reshape how we deliver personalized experiences across Spotify.

What You'll Do

Contribute to the design, development, evaluation, and iteration of recommendation models — including candidate generation, ranking, and embedding models — powering music surfaces at scale.

Drive hands-on ML development to improve reward signals and recommendation quality across Home, Now Playing, and other core surfaces.

Contribute to the team's adoption of generative recommendation models, partnering with ML and AI infrastructure teams.

Promote best practices in ML systems development, testing, and experimentation within the team.

Collaborate with Data Science, Product, and Design partners to define success metrics, run A/B experiments, and translate insights into product improvements.

Partner with teams across Personalization to integrate and test new signals in recommendation systems.

Who You Are

You have a strong background in machine learning and enjoy applying theory to real-world applications, with expertise in statistics and optimization — particularly sequential models, transformers, generative AI, and LLMs.

You have hands-on experience building and shipping production machine learning systems at scale, ideally in personalization or recommendation systems.

You have experience implementing ML systems in Java, Scala, Python, or similar languages. Familiarity with PyTorch, Ray or Hugging Face is a plus.

You have some experience with large-scale distributed data processing frameworks such as Apache Beam, Apache Spark, or Scio, and cloud platforms like GCP or AWS.

You have experience collaborating across teams on complex ML projects and navigating cross-functional stakeholders.

You care about agile software processes, data-driven development, reliability, and disciplined experimentation.

Where You'll Be

This team operates within the Eastern Standard time zone for collaboration

We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location.

The United States base range for this position is $210,000 - $260,000 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. These ranges may be modified in the future.

Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.

At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.

Senior Machine Learning Engineer - Personalization