Jobs at GRAI

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.

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.

Lead the technical vision, architecture, and delivery of large-scale recommendation and personalization systems. Build scalable retrieval, ranking, and real-time pipelines, own ML infrastructure and MLOps, design evaluation and A/B testing, mentor ML engineers, and collaborate with product and engineering to align models with business metrics.

Deliver backend integrations and migration scripts to move users and data to cloud infrastructure. Ensure secure, consistent data transfer, integrate external backends with internal APIs, write tests, and refactor legacy code as needed.

Integrate a partner client application with internal services, migrate users and data to cloud infrastructure, update frontend UI/UX for migration workflows, modify Node.js/TypeScript backend for correct data mapping, and rapidly troubleshoot integration issues during testing and rollout.

Provision cloud environments, build CI/CD pipelines, migrate and configure databases with HA and backups, monitor infrastructure during active data migration, and ensure stable, secure zero-downtime deployments across Dev/Stage/Prod.

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.