At GRAI, we’re building AI systems for sound and music. We’re a small team working on ambitious product and research problems at the intersection of machine learning, audio, and creativity. This role is for someone who wants to work on hard technical problems, move quickly, and help turn strong ideas into real systems.

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