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.

What You'll Be Doing

Close Collaboration: Work side-by-side with research scientists to conduct groundbreaking research in music generation (diffusion, flow matching, or autoregressive models), as well as related domains like ML-based audio processing, music information retrieval, machine learning, and signal processing.
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.

We offer you the flexibility to work where you work best! For this role, you can be within the EMEA region as long as we have a work location.
This team operates within the Central European and GMT time zone for collaboration.
Core working hours are CET 3pm-6pm / EST 9am-12pm.