Spotify
Research Scientist - Personalization
New York, NY
Sponsorship not specified$133k-$190kDetected 29 days ago
Machine LearningLLMsResearchCollaboration
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odds of building a lasting career here
64Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
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About the role
- 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.
Responsibilities
- Develop novel methodologies, models, and evaluation frameworks to advance personalization systems.
- Design and execute rigorous experiments to explore new ideas and validate research hypotheses.
- Collaborate with cross-functional partners to translate research insights into long-term product opportunities.
Requirements
- 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.
Compensation
- $133k-$190k
Benefits
- Conduct original research in machine learning and AI, with a focus on large-scale foundation models, generative AI, and post-training methods, including reinforcement learning.
- You have a Master's or PhD in machine learning, AI, or a related field, or equivalent research experience.
- You have demonstrated expertise in machine learning and artificial intelligence through peer-reviewed publications at conferences such as NeurIPS, ICLR, ICML, KDD, or RecSys.
Company info
- Where You'll Be - This role is based in New York City. - We offer you the flexibility to work where you work best!
- At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone.
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This listing is sourced directly from Spotify's careers page and normalized into a canonical job model.