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Job Details
Posted date: May 21, 2026
Location: Kirkland, WA
Level: Senior
Estimated salary: $200,000
Range: $163,000 - $237,000
Description
Architect data reconciliation frameworks to unify disparate datasets from multiple systems (e.g., resource symphony, allocation data, and fleet guidance), ensuring a single, high-fidelity source of truth for the ML fleet. Audit and resolve data discrepancies across cross-functional team inputs to maintain the integrity of fleet capacity and usage metrics. Develop and maintain complex investigative models that enable real-time, dynamic forecasting of ML fleet supply and demand, accounting for hardware transitions and shifting deployment timelines. Engineer automated dashboards that provide leadership with what-if scenario modeling to visualize the impact of divestments or increased GPU demand on the total fleet. Translate fleet capacity into actionable allocation plans for Product Areas (PAs), ensuring resource distribution aligns with Google’s ML priorities and commitments.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.
The Machine Learning Strategy and Allocations Program Management Office (PMO) sits at the core of Google's AI resource governance, working directly with executive product and AI leadership and driving strategic alignment across the entire AI ecosystem. We partner with all aspects of the company to enable the capacity allocation, planning, and governance for one of the world's largest Machine Learning fleets in the era of AI, ML, and Agents. Our mission is to ensure that Google’s most critical AI products from Gemini and Search to Cloud and YouTube have the right ML compute at the right time to achieve maximum business and technical impact.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. 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.
The US base salary range for this full-time position is $163,000-$237,000 + 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.
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.
Qualifications
Minimum qualifications: Bachelor's degree in a technical field, or equivalent practical experience. 5 years of experience in program management. Experience developing SQL data pipelines and predictive dashboards for advanced analysis. Experience conducting Machine Learning Financial Planning and Analysis (ML FP&A).Preferred qualifications: 5 years of experience managing cross-functional or cross-team projects. Understanding of the business and economic climate of AI/ML technologies. Understanding of hardware transitions (TPU/GPU) and mapping physical data center capacity directly to high-priority AI initiatives. Ability to translate massive fleet capacity into actionable allocation plans that align directly with AI priorities and commitments. Ability to architect automated data reconciliation frameworks.