Principal/Distinguished Engineer, AI Capacity Delivery

As a Principal/Distinguished Engineer for AI Capacity Delivery, you will bridge the gap between low-level hardware design and intelligent software automation. You will architect the intelligent software control plane that manages, validates, and initializes Google’s next-generation global AI fleet. Your core mission is to drastically accelerate the onboarding and validation of Google’s next-generation AI fleet, including TPUs and GPUs. You will deliver and operate highly available, cost optimized data center infrastructure at speed and scale.

You will leverage modern machine learning platforms to build adaptive, self-training systems that analyze physical fleet behavior, predict anomalies, and automatically configure and validate hardware at scale. This person must develop the necessary hardware qualification tests that ensure that our fleet is reliable, properly configured and healthy enough to perform its mission. You will leverage advanced ML platforms to develop self-training systems that analyze physical hardware behavior and guide engineering teams to write highly optimized software for testing.

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

• Lead and architecture the technical strategy for high-performance software control planes that orchestrate, validate, and manage bare-metal systems, custom AI silicon, and distributed data center environments.
• Design and deploy self-training ML-driven monitoring and telemetry systems that automate system validation, replacing legacy test suites with adaptive, self-correcting frameworks.
• Write high-efficiency systems software that automates fleet initialization, minimizing the time it takes to transition physical hardware into live, reliable production capacity.
• Influence the long-term technical roadmap for hardware-software co-design, collaborating closely with TPU, silicon, and distributed systems engineering teams to ensure the reliability and scale of Google's planet scale infrastructure.

Minimum qualifications:

• Bachelor’s degree in Computer Science, Computer Engineering, a related technical field, or equivalent practical experience.
• 15 years of professional software engineering experience, with a focus on systems programming, distributed systems, or high-performance infrastructure orchestration.
• Experience developing low-level systems software, virtualization layers, hypervisors, kernel extensions, or software that interfaces directly with physical infrastructure/bare-metal environments.

Preferred qualifications:

• Master’s degree or PhD in Computer Science, Computer Engineering, or related field.
• Significant background in machine learning platforms and infrastructure, with experience applying AI/ML models to automated system testing, hardware qualification, or predictive system reliability.
• Technical expertise building and validating large-scale distributed systems, bare-metal environments, or hyperscale data center networks.
• Exceptional collaborator with a proven track record of influencing cross-functional teams to drive delivery.
• Track record at a hyperscale cloud provider or AI chipmaker, working on low-level infrastructure software (e.g., hypervisors, hardware abstraction layers, or custom kernels).