Principal Engineer, AI Ecosystem

Google Cloud accelerates organizations’ ability to digitally transform their business with the best infrastructure, platform, industry solutions and expertise. We deliver enterprise-grade solutions that leverage Google’s technology – all on the cleanest cloud in the industry. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

GKE is the industry-leading managed Kubernetes service. As the originator and a large contributor to OSS Kubernetes, our team holds a unique structural position in the industry. Today, the rapid rise of AI and ML workloads is driving a massive paradigm shift, creating entirely new abstractions over Kubernetes and GKE. To lead this evolution, we are expanding our focus into adjacent projects throughout the OSS AI Infrastructure space. Our goal is to leverage our Kubernetes foundation to accelerate innovation globally, and to make GCP the undisputed best place to train and serve accelerated and agentic workloads, seamlessly supporting the frameworks and tools that AI practitioners rely on daily.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

Responsibilities

• Enable massive scale so that ML engineers working primarily in Python can iterate locally and seamlessly scale to 1,000,000+ accelerators without needing to become experts in infrastructure.
• Act as a proxy for the emerging AI/ML end-user persona, evolving the GKE team's intuition and empathy for real-world problems and opportunities within the AI workload lifecycle.
• Drive the development and architectural outlook of a substrate optimized for Accelerated and Agentic Workloads.
• Imagine, architect, and lead the technical execution of industry-defining standards through both direct, direct technical work and by mentoring and guiding teams of engineers.

Minimum qualifications:

• 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.

Preferred qualifications:

• 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, driving technical direction that influences foundational systems.
• Expertise in HPC, distributed workload schedulers (e.g. 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 lines of formal authority, working effectively within highly matrixed environments to align technical and product strategies.