Senior Research Engineer - Music

Spotify•New York, NY

66d•Remote

About The Position

We are seeking a Senior Research Engineer 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 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. For more information, see this press release! https://newsroom.spotify.com/2025-10-16/artist-first-ai-music-spotify-collaboration/

Requirements

You have experience training or fine-tuning large machine learning models on GPUs using PyTorch or similar frameworks.
You have experience working with cloud platforms like Google Cloud Platform, AWS, or Microsoft Azure.
You understand how to debug problems in machine learning training code.
You communicate effectively with global teams and are ready to work both face-to-face and asynchronously with collaborators on multiple continents.
You have experience optimizing code for performance and can make GPUs "go brrr" (train at maximum efficiency).
You learn new concepts and technologies quickly and keep up to date with the rapid pace of development in machine learning and AI.
You are resourceful and proactive; when faced with blockers, you seek out solutions through research, experimentation, and collaboration.
You're not afraid to dig deep into the stack: working with lldb, NVIDIA Nsight, or other low-level debugging tools is a plus.
You have a solid grasp of computer science concepts like type systems, compilers, parallelism, thread safety, encapsulation, and the like.
You have an interest in learning more about audio processing and music information retrieval and you're excited about building amazing products that use these technologies.

Responsibilities

Closely collaborate with research scientists. Work side-by-side to turn new research ideas into well-engineered experiments, ensuring efficiency, clarity, and reproducibility in every implementation.
Improve model training pipelines. You'll debug distributed training, optimize data loading at massive scale, and ensure smooth scaling across compute environments.
Optimize performance. You'll profile and accelerate existing training and inference code to make experiments faster and production systems more responsive.
Integrate models into production environments. You'll work directly with platform and product teams to deploy models into the hands of hundreds of millions of Spotify's users.
Incorporate state-of-the-art research. You'll translate models and techniques described in the literature into robust, well-engineered prototypes.
Maintain a high-quality codebase. You'll enforce clear structure, consistency, and testing practices to support long-term maintainability on a codebase shared between members of a fast-paced globally distributed team.
Enhance researcher experience. You'll build internal tooling, libraries, and workflows to make experimentation, debugging, and deployment more efficient for the whole team.