Technical Program Manager, Off-the-Shelf Hardware

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.

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: $217000 - $236000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Partner with AI researchers to match hardware with model compute, power, bandwidth, and latency needs.
• Establish and manage a data-driven benchmarking framework to assess and down-select commercial off-the-shelf (OTS) hardware.
• Collaborate with engineering teams to build and scale physical and software benchmarking testbeds. Feed OTS performance data and failure analyses into custom engineering designs.
• Work with procurement on sourcing, lead times, and vendor relations; partner with Operations to transition prototypes into field deployment. Own milestones, manage technical trade-offs, mitigate risks, and deliver strategic updates to leadership and cross-functional program management.

Qualifications

Minimum qualifications:

• Bachelor’s degree in an Engineering field or equivalent practical experience.
• 5 years of experience in technical program management or hardware engineering.
• Experience managing hardware evaluation, testing, characterization, or NPI programs.

Preferred qualifications:

• Direct experience with vendor management.
• Familiarity with test infrastructure.
• Understanding of hardware requirements for AI research.
• Knowledge of robotic system design, including kinematics, dynamics, control algorithms, and perception systems.
• Demonstrated ability to thrive in an ambiguous, fast-paced research environment and collaborate effectively across multidisciplinary engineering teams.