Principal Engineer, GKE Platform for AI Inference Workloads

Benefits

• Health, dental, vision, life, disability insurance
• Retirement Benefits: 401(k) with company match
• Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
• Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
• Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
• Baby Bonding Leave: 18 weeks
• Holidays: 13 paid days per year

Preferred working locations: Seattle, WA; Kirkland, WA; Sunnyvale, CA.

Requirements

• 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.

About the role

Google Kubernetes Engine (GKE) is the industry standard for container orchestration and the core of Google Cloud’s modernization strategy. We are now embarking on a mission to reinvent GKE and Kubernetes as the premier substrate for the next generation of computing: AI inference at massive scale. We believe that serving foundation models and large language models represents a paradigm shift in cloud computing. These workloads demand a fundamental rethink of orchestration, moving from CPU-bound micros