What you’ll do
- Work on generative audio systems across models, evaluation, and data
- Design experiments that separate genuine progress from noise
- Build evaluation and dataset pipelines that make model quality measurable and iteration faster
- Make sound trade-offs across quality, latency, reliability, and cost
What we’re looking for
- Comfort taking ownership in ambiguous problem spaces and staying engaged with the problem until it is solved
- Genuine interest in audio, music, and generative modeling
- Strong habits around evaluation, reproducibility, and performance
- Fluency in Python and PyTorch, or similar tools
Especially relevant experience
- Generative modeling, including diffusion, autoregressive methods, or hybrids
- Audio ML, or adjacent experience that transfers well, such as image generation
- Multi-GPU or distributed training
What we offer
- High ownership over important technical work
- Be at the forefront of AI-driven music innovation
- Opportunity to work on infrastructure at scale
- Competitive compensation and equity
- Flexibility in how you work