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

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

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

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