ML Research Scientist, Personalization & Foundation Models

Spotify; Greater London; Hybrid; GBP 90,000 - 140,000; Full time

Job summary

Spotify in London’s Personalization team is advancing ML and AI to shape the future of how listeners discover music and podcasts. The role focuses on large-scale foundation models, generative AI, and post-training methods that improve recommendations.

You will design rigorous experiments, publish research, and translate insights into long-term product opportunities while mentoring teammates and shaping a forward-looking research agenda.

Qualifications

• Master's or PhD in machine learning, AI, or a related field or equivalent research experience.
• Demonstrated expertise via peer‑reviewed publications at NeurIPS/ICLR/ICML/KDD/RecSys.
• Deep knowledge of ML with post-training methods and reinforcement learning for large models.

Responsibilities

• Conduct original research in ML/AI with a focus on large-scale foundation models, generative AI, and post-training methods.
• Develop novel methodologies, models, and evaluation frameworks to advance personalization systems.
• Design and execute rigorous experiments to 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.

Skills

Machine learning

AI research

Reinforcement learning

Experiment design

Large-scale ML

Distributed training

Research publications

Education

Master's degree in ML/AI

PhD in ML/AI (or related field)

Job description

Spotify in London’s Personalization team is advancing ML and AI to shape the future of how listeners discover music and podcasts. The role focuses on large-scale foundation models, generative AI, and post-training methods that improve recommendations.

You will design rigorous experiments, publish research, and translate insights into long-term product opportunities while mentoring teammates and shaping a forward-looking research agenda.