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.

Lead and build GRAI's artist relations function: define strategy, develop onboarding and co-marketing frameworks, run high-value relationships, hire and lead the team, and translate artist insights into product and marketing decisions while partnering across the company.