Lead the architectural direction for llm-d, ensuring a highly optimized, scalable foundation for distributed LLM and Reinforcement Learning (RL) serving across the GKE fleet.

Define GKE's evolution to support massive-scale inference and RL, solving novel orchestration problems in dynamic resource allocation, multi-host TPU/GPU scheduling, and high-throughput networking.

Partner with strategic AI model builders, Deep Mind, and Vertex AI to co-develop an AI-first roadmap, leveraging Google's custom silicon to optimize throughput and compute density.

Lead the broader Kubernetes ecosystem and Open Source Software (OSS) community, driving key upstream initiatives to establish industry standards for AI, RL, and accelerator orchestration.

Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.

15 years of experience in software engineering, or 15 years of experience with an advanced degree.

Experience building distributed systems and driving technical strategy for platform-level infrastructure.

Experience with Kubernetes, container runtimes, and AI/ML infrastructure (e.g., inference serving, LLM, hardware accelerators).