Senior Research Engineer – Music
Spotify
AWSAzureCloudGoogle Cloud PlatformPyTorch
Staff Research Engineer – Music
Spotify
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇸🇪 Sweden
Visit company websiteJob Level
Lead
Tech Stack
AWSAzureCloudGoogle Cloud PlatformPyTorch
About the role
- 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.
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.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningPyTorchGPU trainingcode optimizationdebuggingperformance profilingmodel deploymentcloud platformsparallelismencapsulation
Soft skills
effective communicationcollaborationresourcefulnessproactivityadaptabilityproblem-solvingteamworkasynchronous communicationlearning agilitycreativity