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
- Design, build, evaluate, and ship LLM-based solutions that give users more adaptive control over their listening experience
- Work on prompted playlist experiences with a focus on music fulfillment and session generation
- Collaborate with cross-functional partners across user research, design, data science, product, and engineering
- Prototype new ML approaches and bring them into production at global scale
- Build and improve systems that connect artists and fans in personalized and meaningful ways
- Contribute to the development of scalable ML systems serving hundreds of millions of users
- Promote best practices in ML system design, testing, evaluation, and deployment across the organization
- Actively contribute to a strong community of machine learning practitioners at Spotify
Who You Are
- You are experienced in machine learning and enjoy solving complex real-world problems in collaborative environments
- You have a strong background in machine learning, natural language processing, and generative AI
- You are comfortable applying theory to build real-world, production-ready applications
- You have hands-on experience building and deploying end-to-end ML systems at scale
- You are familiar with LLM-based systems and techniques for improving them using human feedback such as reinforcement fine-tuning, DPO, or similar approaches
- You have experience designing modular ML architectures and writing technical specifications in partnership with product teams
- You are experienced with large-scale distributed data processing tools such as Apache Beam or Apache Spark
- You have worked with cloud platforms like GCP or AWS
Where You'll Be
- This role is based in New York
- We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.