67 Google Program Management Jobs Hiring Near You

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X Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; New York, NY, USA.
Minimum qualifications:

• Bachelor's degree in a technical field, or equivalent practical experience.
• 5 years of experience in program management.
• Experience in infrastructure resource management or Infrastructure capacity planning.
• Experience working with data analytics tools like SQL, Python, Databases, or other programming languages.

Preferred qualifications:

• 5 years of experience managing cross-functional or cross-team projects.
• Experience in large scale, distributed infrastructure.
• Experience with deploying large language models or distributed machine learning.
• Domain expertise in supply chain management or data center capacity planning, compute/storage infrastructure.

About the job
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 Resource Engineering (MLRE) team serves as the stewards of roughly half of Google's global ML accelerator fleet. As an integral pillar of the broader ML Fleet organization, we are responsible for managing the Google-wide Large Language Model (LLM) Serving pool. In addition, we act as the dedicated Product Area Resource Management (PARM) partners for critical client groups across Alphabet.
As a PARM group, our core mission centers on planning, deploying, and maximizing the efficiency of TPU and GPU capacity in strict alignment with Google's top strategic priorities.
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.
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 .
Responsibilities

• Act as a trusted advisor to Product Area partners, understanding their TPU/GPU requirements and delivering a guided, seamless resource management experience.
• Collaborate closely with Software Engineering (SWE) and Site Reliability Engineering (SRE) teams to uncover, analyze, and execute on efficiency opportunities across our managed resource footprints.
• Own the operational execution of capacity allocations and allied workflows using core Google tooling, a technical or engineering background is critical to successfully navigating this significant operational component.
• Partner cross-functionally to drive tool and process optimizations. Leverage strong data analysis skills to convert fleet metrics into actionable business value and automated scalability.
• Utilize an understanding of ML fundamentals to inform resourcing decisions, with a preference for practical experience in deploying large-scale ML models.

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