Technical Program Manager, AI for Science
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Minimum qualifications:
- Bachelor’s degree in Computer Science, Engineering, Mathematics, Computational Biology, Physics, a related quantitative/scientific discipline, or equivalent practical experience.
- 5 years of experience in technical program management, engineering program management, or technical leadership in advanced research, machine learning, or distributed systems environments.
Preferred qualifications:
- Demonstrated experience partnering directly with research scientists or technical leads to translate exploratory, ambiguous initiatives into concrete execution roadmaps.
- Strong conceptual grasp of modern AI/ML systems (e.g., large language models, multi-agent frameworks, or scientific computing pipelines) and the ability to evaluate technical trade-offs.
- Exceptional technical communication and stakeholder management skills, with a track record of driving cross-functional consensus across multidisciplinary teams.
- A deep passion for accelerating scientific discovery, clean energy, and health through artificial intelligence.
About the job
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.
Responsibilities
- Lead and shape complex programs at the intersection of foundational models, AI agents, and scientific research within DeepMind’s SSI Initiatives Unit.
- Act as a trusted thought partner to research, engineering leads, product managers, and the wider program management team.
- Help navigate the deep ambiguity inherent in breakthrough scientific research through strong program definition, execution, and technical delivery.
- Drive the integration of autonomous agents, reasoning loops, and sandboxed research tooling into scientific workflows.
- Collaborate across research pillars, from biology and quantum chemistry to automated theorem proving and compute infrastructure. Ensure scientific teams have the operational rhythm, technical alignment, and resource access needed to maximize research velocity.
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