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Open nowFirst seen 4 hours ago

Technical Program Manager, Gemini Post-Training (Physical Engineering)

Google3,388 open roles

Where
Mountain View, CA, USA; New York, NY, USA
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Your applicationOpen nowTechnical Program Manager, Gemini Post-Training (Physical Engineering)Google · Mountain View, CA, USA; New York, NY, USA

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. Google postings stay open a median of 29 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.7%14 days
  5. 35.6%30 days
  6. 58.0%60 days
This job: first seen 4 hours ago

Google median: 29 days open

The posting

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; New York, NY, USA.

Minimum qualifications:

  • Bachelor's degree in a technical field, or equivalent practical experience.
  • 8 years of experience in program management.

Preferred qualifications:

  • 8 years of experience managing cross-functional or cross-team projects.
  • Experience managing post-training or agentic tool-use programs in fast-paced environment.
  • Experience coordinating domain-expert data acquisition, vendor operations, or verifiable simulation environments across physical engineering domains.
  • Knowledge of software, hardware/physical engineering, and machine learning development life-cycles.

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.

As a Technical Program Manager in Gemini Post-Training (Physical Engineering), you will apply program management methodologies across multiple post-training data, evaluation, simulation environment, harness, and modeling initiatives. You will partner with researchers, engineers, and product managers to define project scope, goals, and timelines, turning research priorities into high-quality training data flywheels and deliverable model milestones. You will drive programs to enable Gemini to generate executable, physically valid, and spec-compliant engineering designs.

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: $192000 - $278000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

  • Organize and optimize post-training initiatives across hardware, physical systems, and infrastructure into clear goals, milestones, and release plans.
  • Scope and execute domain-expert data acquisition, design ingestion pipelines, synthetic task generation, and RL environments.
  • Partner with modeling leads to build customer-centric benchmarks and simulation/rule verifiers grading executability, physical well-formedness, and spec satisfaction.
  • Manage project planning using lightweight methodologies to improve research velocity, release predictability, and align cross-functional dependencies.
  • Evaluate technical trade-offs between geometric plausibility versus true functional engineering correctness, manufacturability constraints, and reward hacking risks.
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