Technical Program Manager, Compute Management, DeepMind

Technical Program Manager, Compute Management, DeepMind Share Technical Program Manager, Compute Management, DeepMind

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corporate_fareDeepMindplaceMountain View, CA, USA

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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:

• Background in technical program management, capacity planning, or large-scale ML infrastructure operations, backed by direct proficiency with SQL, Python, and quantitative capacity modeling.
• Advantageous background includes a relevant technical degree, familiarity with cluster scheduling and distributed ML infrastructure, data center knowledge, and experience building lightweight workflow automation.
• Excellent technical communication skills to distill complex ideas, along with expertise in navigating cross-functional processes and solving problems with repeatable, scalable solutions.
• Strong stakeholder management abilities with comfort to thrive and adapt in dynamic environments aligned with DeepMind's mission.

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. The role sits within DeepMind’s Compute Planning team. The team is responsible for building, allocating, and managing DeepMind’s extensive Machine Learning (ML) accelerator and compute portfolio across TPU and GPU clusters supporting Gemini pre-training, post-training, evaluations, and serving. As a Technical Program Manager in the Compute Planning team, you will play a key role in the planning, allocation, and ongoing management of DeepMind’s extensive ML compute portfolio. You will work alongside our Research Leads, Product Managers, Engineers, and Researchers to help define and optimize DeepMind's overall compute capacity, fleet utilization, and execution velocity in this space.

Artificial intelligence will be one of humanity’s most transformative inventions. At 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: $138000 - $197000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google. Responsibilities

• Drive multi-quarter resource forecasting and allocation for machine learning hardware (TPUs and GPUs), host and storage systems, and cloud infrastructure to support scaling initiatives.
• Partner closely with research and engineering leads to model operational trade-offs and distribute compute capacity efficiently across pre-training, post-training, evaluations, and serving priorities.
• Coordinate end-to-end compute readiness and seamless cluster transitions for flagship training runs while actively managing dedicated reservations and dynamic quota pools.
• Monitor and optimize overall fleet occupancy, hardware resource availability, scaling efficiency, and workload performance to rapidly resolve scheduling bottlenecks.
• Develop robust self-serve tooling, including SQL and Python data pipelines, live operational dashboards, actionable playbooks, and data-backed recommendations for leadership.

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