Research Scientist - Generative Audio

We are seeking Research Scientists (across all levels of seniority) to join our Artist-First AI Music Lab. Our team pioneers and advances state-of-the-art generative technologies for music that create breakthrough experiences for fans and artists. We invent entirely new listening experiences that center and celebrate artists and creatives. All of our products will put artists and songwriters first, through these four principles: Partnerships with record labels, distributors, and music publishers: We’ll develop new products for artists and fans through upfront agreements, not by asking for forgiveness later. Choice in participation: We recognize there’s a wide range of views on the use of generative music tools within the artistic community. Therefore, artists and rightsholders will choose if and how to participate to ensure the use of AI tools aligns with the values of the people behind the music. Fair compensation and new revenue: We will build products that create wholly new revenue streams for rightsholders, artists, and songwriters, ensuring they are properly compensated for the use of their work and transparently credited for their contributions. Artist-fan connection: AI tools we develop will not replace human artistry. They will give artists new ways to be creative and connect with fans. We will leverage our role as the place where more than 700 million people already come to listen to music every month to ensure that generative AI deepens artist-fan connections. Learn more in our press release: https://newsroom.spotify.com/2025-10-16/artist-first-ai-music-spotify-collaboration/

What You'll Do:

Conduct groundbreaking research in generative audio using diffusion or flow matching models, with a focus on one or more of the following areas:

Vocal Synthesis — Research in vocal and speech synthesis, along with related areas such as ML-based audio processing and signal processing.

Post-Training — Research in post-training techniques for music generation, including preference alignment methods (such as DPO, RLHF, or KTO), reward model design and training, and reinforcement learning to improve output quality, controllability, and human preference adherence.

Editing — Research in iterative music generation and audio editing, including capabilities such as stem replacement, instrumentation change, mood changes (while preserving content), tempo changes, and structure changes.

You will also:

• Run large-scale experiments with access to Spotify's extensive infrastructure and an audience of more than 700 million monthly active users.