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