Teams at GRAI

Design and implement large-scale retrieval and ranking architectures for personalized recommendations. Build end-to-end ML systems (data processing, feature engineering, training, deployment, monitoring), run A/B tests and offline evaluations, collaborate with product and engineering teams, and continuously monitor and improve model performance.

Develop and evaluate generative audio models, design experiments to measure true progress, and build evaluation and dataset pipelines. Optimize trade-offs across quality, latency, reliability, and cost while owning ambiguous research problems and iterating on infrastructure at scale.

Own and ship signature interaction patterns and motion-driven UX for mobile (iOS/Android). Turn concepts into production-quality interfaces, build and maintain a scalable design system, prototype rapidly, and bridge design and engineering while setting the craft bar for the team.