Spotify
Personalization
SeniorMachineLearningEngineer-Personalization
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“Senior Machine Learning Engineer - Personalization at Spotify. Skills: Machine Learning, recommendation systems, generative AI, LLMs, Python. design, development, evaluation, and iteration of recommendation models. candidate generation”
What You'll Achieve.
make deciding what to play next on Spotify easier and more enjoyable for every listener; make great recommendations to every individual and keep the world listening; improve reward signals and recommendation quality; translate insights into product improvements
Industry & Context.
operate within the Eastern Standard time zone for collaboration, can be within the North America region
What They're Looking For.
Must Have
background in machine learning, expertise in statistics and optimization, sequential models, transformers, generative AI, LLMs, 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 such as Apache Beam, Apache Spark, or Scio, experience with cloud platforms like GCP or AWS, experience collaborating across teams on complex ML projects, navigating cross-functional stakeholders, agile software processes, data-driven development, reliability, disciplined experimentation
Nice to Have
personalization or recommendation systems experience, Familiarity with PyTorch, Ray or Hugging Face
What You'll Do.
and iteration of recommendation models
powering music surfaces at scale
hands-on ML development to improve reward signals and recommendation quality
Contribute to the team's adoption of generative recommendation models
Promote best practices in ML systems development
How You'll Work.
Team & Collaboration
Collaborate with Data Science, Product, and Design partners; Partner with teams across Personalization to integrate and test new signals in recommendation systems; Partnering with ML and AI infrastructure teams
Full Job Description
## Description 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 sequ
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