The Personalization (PZN) team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music, podcasts and audiobooks 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 all over the world use the products we build which include destinations like Home and Search as well as original playlists such as Made For You, Discover Weekly and Daily Mix.
Our team’s mission is to bring emerging search and agentic experiences to a mature state: exploring, defining, building, validating and optimizing new ideas. These can include new content types in our Search engine or emerging user interaction patterns.
What You'll Do
Contribute to designing, scaling/building, evaluating, integrating, shipping, and refining reward signals for recommendations by hands-on ML development.
Lead collaborations and align across Personalization to integrate and A/B test mid-term signals in various recommendation systems.
Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization.
Who You Are
You have a background in machine learning, enjoy applying theory to develop real-world applications, with experience in statistics and optimization, especially in sequential models, transformers, generative AI and large language models, and relevant fine-tuning processes.
You have hands-on experience with large cross-collaborative machine learning projects and managing stakeholders.
You have hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages.
You have some experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, or even our open source API for it - Scio, and cloud platforms like GCP or AWS.
You care about agile software processes, data-driven development, reliability, and disciplined experimentation.
Where You'll Be
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.
This team operates within the Eastern Standard time zone for collaboration.
Skills Required
- Background in machine learning
- Experience in statistics and optimization
- Hands-on experience with ML projects
- Experience implementing production ML systems
- Familiarity with data processing frameworks
- Knowledge of cloud platforms
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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