Technical Program Manager, Compute Planning, DeepMind
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Minimum qualifications:
- Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.
- 5 years of experience in technical program management in an infrastructure or AI environment.
- Experience in Python and SQL to query data or script automations.
Preferred qualifications:
- Experience managing compute capacity at Google (compute infrastructure) or in industry across other cloud providers (e.g., GCP).
- Knowledge of data center infrastructure and operations.
- Ability to communicate complex technical concepts clearly to various sets of teams.
- Strong relationship-building and cross-functional collaboration skills.
- Initiative to solve problems with solutions that are repeatable, scalable and sustainable.
About the job
As a Technical Program Manager (TPM) in the Compute Planning team, you will play a key role in the end-to-end planning and ongoing management of DeepMind’s extensive ML Hardware portfolio.
You will be joining a high-impact team of TPMs to work alongside our Product Managers, Engineers and Researchers to help define and optimize DeepMind's overall capabilities in the compute 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.
US: $217000 - $236000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Lead planning, tracking, and management of the compute fleet at scale.
- Own end-to-end programs optimizing CPU, RAM, and TPU workloads to deliver clear organizational Return on Investment (ROI).
- Partner with leadership to balance costs, utilization, and research velocity for key priorities.
- Bridge Finance, Engineering, and Research teams, translating complex compute data into executive decision-making.
- Debug infrastructure bottlenecks, scale self-serve documentation, and drive internal AI adoption (Gemini Enterprise, internal APIs) for tooling.
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