Research Scientist - Personalization & Generative AI

Spotify; New York (NY); Hybrid; USD 133,000 - 190,000; Full time

Benefits offered by this job

Health insurance

Six months paid parental leave

401(k) retirement plan

Meal allowance

23 paid days off

13 paid flexible holidays

Job summary

Spotify is seeking a Research Scientist for the Personalization team in New York City to advance large-scale ML and AI for recommendations like Blend and Discover Weekly. You will conduct original research in foundation models, post-training methods and reinforcement learning, design experiments, publish results, and partner with cross-functional teams to translate insights into product opportunities.

The role combines research excellence with real-world impact.

Qualifications

• Master’s or PhD in machine learning, AI, or related field, or equivalent research experience.
• Peer-reviewed publications at NeurIPS, ICLR, ICML, KDD, or RecSys.
• Deep knowledge of ML/AI with hands-on post-training and RL for large models; reward design, distributed training, evaluation.

Responsibilities

• Conduct original research in ML/AI focusing on foundation models, generative AI, and RL.
• Develop novel methodologies, models, and evaluation frameworks for personalization systems.
• Design and run rigorous experiments to validate ideas and hypotheses.
• Contribute to the scientific community through publications and talks.
• Collaborate with cross-functional partners to translate research into product opportunities.
• Help define and evolve a forward-looking research agenda aligned with Spotify’s personalization strategy.
• Mentor others and foster a collaborative research culture.

Skills

Foundation models

Reinforcement learning

Large-scale ML

Experiment design

Collaboration

Education

Master or PhD in ML/AI

Job description

Spotify is seeking a Research Scientist for the Personalization team in New York City to advance large-scale ML and AI for recommendations like Blend and Discover Weekly. You will conduct original research in foundation models, post-training methods and reinforcement learning, design experiments, publish results, and partner with cross-functional teams to translate insights into product opportunities.

The role combines research excellence with real-world impact.