Role overview
Join our global force of 500+ innovators, blending the latest in tech with the greatest in soundtracking, from our Stockholm HQ to offices in London, New York, Los Angeles, Berlin, Paris, Oslo, and Seoul. We’re an industry leader with a startup mentality. We take what we do seriously, but we don’t take ourselves too seriously. Creating and collaborating to transform the sound of streaming, content, and culture. Come join us, and let the world feel your work.
We’re looking for an experienced Engineering Manager to lead the ML Soundtrack teams focused on next-generation AI music adaptation and foley generation. This is a pivotal role where you will be accountable for people, delivery, process, and cross-team technical leadership, with a core mandate to ship and scale cutting-edge diffusion models for music. You will own the complete path from innovative research to low-latency, high-fidelity production, driving one of our most critical technical initiatives.
You’ll operate within' a core priority area in our Next-Generation Soundtracking domain, leading ML/AI teams. A key part of your impact will be to grow, shape, and mentor engineers as we scale our generative AI capabilities. You will manage dependencies and collaborate closely with our data platform, MLOps, and product engineering teams to integrate these powerful ML solutions into our end-user experiences.
What you'll work on
- Own the technical roadmap and model strategy for generative music, including diffusion and transformer-based approaches.
- Lead the full lifecycle from research to production, championing training, evaluation, and deployment for real-time inference.
- Drive the productionisation of inference through model optimization (distillation, quantization), caching, and cost controls.
- Build and maintain team health through effective rituals, 1:1s, and fostering a psychologically safe, high-ownership culture.
- Manage cross-team dependencies and delivery with data, MLOps, and product engineering teams.
What we're looking for
- Deep ML engineering background with hands-on experience in generative diffusion models for audio/music (including PyTorch and modern training stacks).
- Proven experience deploying ML systems into production at scale, with a focus on latency, stability, and cost.
- Strong ML system design and architecture skills across the full machine learning lifecycle.
- Track record of managing engineering teams
- Demonstrated ability to set clear goals, manage performance, and grow engineers through mentorship and feedback.