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