Teams at GRAI

Build and maintain production systems for AI music products, including backend services, APIs, workers, media pipelines, model inference infrastructure, queues, scheduling, storage, and observability. The role requires debugging across software and infrastructure boundaries, optimizing distributed systems, and owning technical projects end-to-end. Relevant experience includes media processing, ML infrastructure, cloud platforms, Kubernetes, Docker, Terraform, and large-scale data pipelines.

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.

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.