Technical Program Manager III, Technical Infrastructure, Cloud Systems

Google•Sunnyvale, CA

19h•$163,000 - $236,000

About The Position

A problem isn’t truly solved until it’s solved for all. That’s why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you’ll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You’ll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers. Our team leads technical program management for the core compute and capacity orchestration infrastructure powering Search, YouTube, Google Cloud, and Gemini spanning cluster scheduling, global workload placement, data center capacity optimization, runtime virtualization, and next-generation AI hardware rollouts. Static multi-year roadmaps no longer exist here, priorities move at high velocity across dozens of concurrent initiatives. We need a technical program leader to own end-to-end functional slices across multiple layers of the stack and connect the dots across teams bringing disciplined intake and relentless follow-through so stalled programs cross the finish line and legacy systems get decommissioned. In this role, you will partner with senior engineers to spot technical collisions early (such as efficiency vs. reliability trade-offs), report unvarnished truth on program risks, and replace fragmented tracking silos and manual toil with automated, AI-driven single sources of truth. 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. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google [https://www.google.com/about/careers/applications/benefits/].

Requirements

Bachelor's degree in a technical field, or equivalent practical experience.
5 years of experience in program management.
Experience with systems or technical infrastructure.
Experience as an engineer (e.g., Site Reliability Engineer or Software Engineer, etc.).

Nice To Haves

5 years of experience managing cross-functional or cross-team projects.
Experience owning end-to-end functional slices of infrastructure stack across distributed cluster management, cloud infrastructure, operating systems/virtualization, networking, and hardware/software platform launches through legacy decommissioning.
Experience consolidating fragmented tracking silos into a single automated/AI-assisted source of truth.
Experience connecting dots across organizational boundaries, closing unowned integration seams, and structuring customer intake and OKR/QBR cadences.
Thrives in ambiguity and ability to ramp rapidly on unfamiliar domains, understand multi-layer system trade-offs, query telemetry/SQL directly, and surface technical risks early with engineering leads.

Responsibilities

Own horizontal functional slices across multiple layers of the infrastructure stack from hardware and kernel/runtime up through cluster scheduling, capacity control planes, and customer workloads. Grasp system-level trade-offs (such as compute utilization vs. memory headroom and reliability), surface hidden technical collisions early, and deliver risk assessments.
Operate in an environment where static multi-year roadmaps have given way to rapid priority shifts and urgent escalations. Establish disciplined intake gates, work-in-progress limits, and decision lock-in so programs don't stall.
Replace repetitive status-reading meetings and manual toil with a single live source of truth, AI-assisted workflows, and concise decision artifacts that busy engineers and leads can review and approve rapidly.
Replace reactive escalations with structured cross-organization partner intake, rigorous OKR/QBR cadences, and measurable infrastructure outcomes across compute efficiency, capacity yield, hardware launch velocity, and production reliability.