Job Description

Minimum qualifications

• Bachelor’s degree in Computer Science, Mathematics, Electrical Engineering, related technical field, or equivalent practical experience.
• 2 years of experience in program management.

Preferred qualifications

• Experience across computing infrastructure, distributed systems, or machine learning infrastructure, with a working understanding of how large-scale platforms are designed, operated, and scaled reliably.
• Excellent communication skills, ability to translate highly technical concepts for cross-functional stakeholders, paired with deep interest in foundational models, AI agents, and how large-scale infrastructure unlocks the next AI breakthroughs.
• Strong organizational skills to prioritize and drive multiple high-priority workstreams in an ambiguous, fast-paced environment, with minimal direction.
• Proficiency in Python, SQL, and Sheets for data analysis, reporting, lightweight automation, and internal tool configuration.

About the job

Google's projects, like our users, span the globe and require managers to keep the big picture in focus while being able to dive into the unique engineering challenges we face daily. As a Technical Program Manager at Google, you lead complex, multi-disciplinary engineering projects using your engineering expertise. You plan requirements with internal customers and usher projects through the entire project lifecycle. This includes managing project schedules, identifying risks and clearly communicating them to project stakeholders. You're equally at home explaining your team's analyses and recommendations to executives as you are discussing the technical trade-offs in product development with engineers.

Using your extensive technical and leadership expertise, you manage projects of various size and scope, identifying future opportunities, improving processes and driving the technical directions of your programs.

We are looking for someone to join our GenAI compute team to primarily coordinate the GenAI serving process and support compute needs for Gemini training. You will work directly with teams on their serving requests and help improve and automate these workflows. In this role you will resolve bottlenecks and align cross-functional teams on high-priority milestones.

As part of the Gemini compute team, you will coordinate and automate AI serving review requests, ensuring teams meet criteria to launch smoothly at scale. You will also support operations and governance of Gemini training resources (CPU/RAM/Disk/Accelerators). Occasional schedule flexibility is required to support global model releases.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $184000 - $197000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

As part of the Gemini compute team, you will coordinate and automate AI serving review requests, ensuring teams meet criteria to launch smoothly at scale. You will also support operations and governance of Gemini training resources (CPU/RAM/Disk/Accelerators). Occasional schedule flexibility is required to support global model releases.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Responsibilities

• Debug infrastructure issues, adjust configurations, and advise teams on compute governance.
• Vet requests, coordinate launches, and guide teams through milestones.
• Identify workflow bottlenecks, develop internal tooling (dashboards, scripts), and write scalable documentation.
• Use data and logic to bring clarity to complex discussions without formal authority.