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.
Skills Required
- Strong background in machine learning with expertise in statistics and optimization
- Hands-on experience building and shipping production machine learning systems at scale
- Experience implementing ML systems in Java, Scala, Python, or similar languages
- Experience with large-scale distributed data processing frameworks like Apache Beam, Apache Spark
- Experience with cloud platforms like GCP or AWS
- Experience collaborating on complex ML projects
- Care about agile software processes, data-driven development, and disciplined experimentation
Spotify Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Spotify and has not been reviewed or approved by Spotify.
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Flexible Benefits — Employees consistently praise the total compensation package beyond base salary, highlighting a mix of RSUs, cash incentives, and stipends alongside core pay. The package is described as flexible and customizable through equity choices (e.g., RSUs, options, cash) that can be tailored for long-term wealth building.
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Leave & Time Off Breadth — Time-off offerings are repeatedly highlighted as substantial, including generous vacation, paid sick days, volunteer time, and flexible holidays. These policies are framed as a meaningful part of the overall rewards experience beyond salary.
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Healthcare Strength — Health coverage is portrayed as comprehensive, spanning medical, dental, vision, life insurance, disability coverage, and mental health support. Additional employer contributions to HSAs are cited as strengthening the overall health and wellness value proposition.
Spotify Insights
What We Do
Spotify transformed music listening forever when it launched in Sweden in 2008. Discover, manage and share over 50m tracks for free, or upgrade to Spotify Premium to access exclusive features including offline mode, improved sound quality, and an ad-free music listening experience.
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