Technical Program Manager III, ML Infrastructure Resource Management, Google Cloud

Google•Sunnyvale, CA

10d•$163,000 - $237,000•Onsite

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.