About the role
The Music Mission team owns Spotify’s end to end proposition for music creators and the experiences they create for fans.
The team is dedicated to building tools and services to enable creation, promotion, expression, and monetization at scale. Our Artist-First AI Music Lab designs and builds state-of-the-art generative AI products for music that create breakthrough experiences for fans and artists. We are currently searching for a Machine Learning Engineer to join our journey as we invent entirely new listening experiences that center and celebrate artists and creatives. All of our products put artists and songwriters first through four guiding 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 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 uses 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. \n What You’ll Do Design, build, evaluate, and improve machine learning training and inference pipelines that power new AI-driven music experiences and help take them to fully scaled production-ready features. Apply machine learning and prompt engineering knowledge across complex ML pipelines to support rich user experiences involving large language models. Create evaluation frameworks, including LLM-as-judge pipelines, to measure quality and build fast feedback loops that enable rapid and confident iteration. Partner with music subject-matter experts to bootstrap training and reference data, including synthetic generation, expert curation, and taxonomy design. Build scalable systems that balance experimentation velocity with production rigor, ensuring strong performance, reliability, and latency at Spotify scale. Collaborate closely with Data Science teams to connect evaluation frameworks with real-world usage signals and continuously improve model quality. Contribute to technical direction and engineering best practices across model deployment, observability, experimentation, and production infrastructure. Work cross-functionally with engineering, product, design, and music industry partners to shape entirely new listening experiences for artists and fans.
