Lead the evolution of music discovery through machine learning by managing the Surfaces Music team, responsible for powering key recommendation systems across Spotify’s most prominent user interfaces. This role centers on building and refining models that shape how listeners encounter music, from new releases to long-tail favorites.
Key Responsibilities
- Develop and scale machine learning systems that drive recommendations in high-visibility areas such as the Home feed and Now Playing screen
- Design and optimize candidate generation, ranking, and embedding pipelines to improve relevance and personalization
- Advance the use of transformer-based architectures to shape next-generation discovery experiences
- Enable users to find both trending tracks and hidden gems by leveraging deep catalog understanding
- Partner with teams in Personalization, User Experience, and Music to align technical innovation with product goals
Technology Focus
The team leverages modern deep learning techniques, with a strong emphasis on transformer-based models, to enhance how music is surfaced. Work spans the full lifecycle of model development—from candidate retrieval to final ranking—ensuring recommendations are both accurate and contextually meaningful.
