Principal Engineer, GKE Platform for AI Inference Workloads

• 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, DeepMind, 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.

Minimum qualifications:

• 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).

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• Experience interacting with senior customer stakeholders (CTOs, chief architects) to represent the technical vision of the organization.
• Demonstrated track record of significant technical contributions to the Kubernetes open-source project or related CNCF AI/ML projects (e.g., Kueue).
• Demonstrated track record of influencing cross-functional teams (product, engineering, research) to deliver complex technical outcomes.
• Deep technical understanding of high-performance networking (RDMA, NCCL), storage/caching architectures for massive model weights, and accelerator virtualization/sharing mechanisms.

• 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).