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