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