<p>Apply</p> <p>share</p> <ul> <li>linkCopy link </li><li>emailEmail a friend </li></ul> <p>info_outline</p> <p>XNote: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; Kirkland, WA, USA; Seattle, WA, USA.</p> <p>Minimum qualifications:</p> <ul> <li> <p>Bachelor's degree in a technical field, or equivalent practical experience.</p> </li><li> <p>5 years of experience in program management.</p> </li><li> <p>Experience in resource management.</p> </li><li> <p>Experience in customer facing communication, stakeholder management and supply chain operations.</p> </li></ul> <p>Preferred qualifications:</p> <ul> <li>5 years of experience managing cross-functional or cross-team projects. </li><li>Experience with Machine Learning infrastructure, accelerators (TPUs/GPUs), or managing AI/ML workloads at scale. </li><li>Experience with capacity management, supply chain, or demand forecasting processes in a technology context. </li><li>Experience in defining and implementing governance frameworks or policies for technical resources. </li><li>Ability to navigate ambiguity, influence without direct authority, and drive consensus across technical and non-technical teams. </li></ul> <p>About the job</p> <p>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.</p> <p>The team's mission is to ensure Alphabet's deployed fleet of ML Accelerators (e.g., TPUs, GPUs) and supporting Auxiliary Compute, Storage, and Networking infrastructure delivers Return on Investment throughout the hardware lifecycle.</p> <p>Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.</p> <p>The US base salary range for this full-time position is $156,000-$229,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.</p> <p>Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.</p> <p>Responsibilities</p> <ul> <li>Lead cross-functional programs related to ML Fleet capacity management, including the design, update, and maintenance of ML Fleet's cluster-level allocation Plan of Record. </li><li>Drive the development, implementation, and ongoing maintenance of fleet-wide accelerator and auxiliary resource usage metrics, policies, and governance frameworks. </li><li>Oversee and optimize chip/aux operations necessary to maintain a healthy and efficient ML fleet and Global Quota Marketplace. </li><li>Manage communications and escalations related to ML resource allocation, performance, and strategic shifts for Product Areas and other partners. </li><li>Identify gaps and drive initiatives to improve existing tooling and processes, enhancing the efficiency, agility, and responsiveness of ML capacity allocation and management. </li></ul>