Spotify
Research Scientist - Personalization
datafull-timeNew York, NY
SALARY
Not listed
WORK TYPE
remote
JOB TYPE
full-time
INDUSTRY
general
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About the role
What You'll Do
- 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.
- Develop novel methodologies, models, and evaluation frameworks to advance personalization systems.
- Design and execute rigorous experiments to explore new ideas and validate research hypotheses.
- Contribute to the scientific community through publications, talks, and conference participation.
- Collaborate with cross-functional partners to translate research insights into long-term product opportunities.
- Help define and evolve a forward-looking research agenda aligned with Spotify’s personalization strategy.
- Mentor others and contribute to a strong, curious, and collaborative research culture.
Who You Are
- 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.
- You have deep knowledge of machine learning, with hands-on experience in areas such as post-training and reinforcement learning for large models, reward design, large-scale distributed training, or evaluation of model behavior.
- You are experienced in designing experiments and working with real-world datasets to validate research ideas.
- You care about advancing understanding of user behavior and improving experiences across music and talk content.
- You bring curiosity, creativity, and a thoughtful approach to solving complex, open-ended problems.
- You value collaboration and actively seek diverse perspectives in your work.
Where You'll Be
- This role is based in New York City.
- 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.
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