Principal Engineer, AI Ecosystem

Google•Kirkland, WA

5d•$307,000 - $427,000

About The Position

Google Cloud provides enterprise-grade infrastructure, platform, and industry solutions to customers in more than 200 countries and territories. GKE is Google Cloud’s managed Kubernetes service and a major contributor to open-source Kubernetes. The rise of AI and machine-learning workloads is creating new abstractions over Kubernetes and GKE, prompting the organization to expand into adjacent open-source AI infrastructure projects. The goal is to use Google’s Kubernetes foundation to accelerate innovation and make Google Cloud a leading platform for training and serving accelerated and agentic workloads, while supporting the frameworks and tools used by AI practitioners. The Principal Engineer is a high-impact individual contributor within the GKE organization. The role applies expertise in AI/ML frameworks to strengthen technical leadership and the team’s understanding of AI workloads. It serves as a technical bridge between GKE infrastructure and the open-source AI ecosystem, including Ray, Slurm, KubeFlow, PyTorch, NumPy, and CUDA. With an understanding of AI end users, the engineer guides the architectural direction of Kubernetes and related projects. The broader AI and Infrastructure team delivers AI and infrastructure capabilities at scale, efficiency, reliability, and velocity, supporting Google customers, employees, and users worldwide.

Requirements

Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
15 years of experience in software engineering, focusing on technical innovation, large-scale systems operations, or engineering leadership.
10 years of experience working with distributed systems, ML/AI infrastructure, deep learning frameworks, or large-scale compute orchestration.

Nice To Haves

Master’s degree or PhD in Computer Science, Artificial Intelligence, High-Performance Computing, or a related field.
Experience operating at a company-wide or multi-organization level and driving technical direction that influences foundational systems.
Expertise in HPC, distributed workload schedulers such as Slurm, and managing complex LLM training and inference workloads at massive scale.
Familiarity with cloud-native orchestration frameworks and an understanding of how to bridge traditional HPC methodologies with modern cloud infrastructure.
Ability to influence outside formal lines of authority and work effectively in highly matrixed environments to align technical and product strategies.

Responsibilities

Enable ML engineers working primarily in Python to iterate locally and scale seamlessly to 1,000,000+ accelerators without needing to become infrastructure experts.
Represent the emerging AI/ML end-user persona and develop the GKE team’s understanding of real-world problems and opportunities across the AI workload lifecycle.
Drive development and architectural direction of a substrate optimized for accelerated and agentic workloads.
Imagine, architect, and lead technical execution of industry-defining standards through direct technical work and by mentoring and guiding engineering teams.