Technical Program Manager, Gemini Post-Training (Physical Engineering)
About The Position
As a Technical Program Manager at Google, you lead complex, multidisciplinary engineering projects using engineering expertise. You plan requirements with internal customers and guide projects through their lifecycle, including managing schedules, identifying risks, and communicating with stakeholders. You explain analyses and recommendations to executives and discuss technical trade-offs in product development with engineers. You manage projects of varying sizes and scopes, identify future opportunities, improve processes, and drive technical directions. In Gemini Post-Training (Physical Engineering), you will apply program management methodologies across 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, translating research priorities into high-quality training data flywheels and model milestones. The programs aim to enable Gemini to generate executable, physically valid, and specification-compliant engineering designs. Google DeepMind is an interdisciplinary AI lab advancing AI to address global challenges and support product innovation. The organization emphasizes public benefit, scientific discovery, safety, and ethics. Individual pay is determined by job-related skills, experience, and relevant education or training. The US salary range is $192,000–$278,000, plus a 20% bonus target, equity, and benefits.
Requirements
- Bachelor's degree in a technical field, or equivalent practical experience.
- 8 years of experience in program management.
Nice To Haves
- 8 years of experience managing cross-functional or cross-team projects.
- Experience managing post-training or agentic tool-use programs in a fast-paced environment.
- Experience coordinating domain-expert data acquisition, vendor operations, or verifiable simulation environments across physical engineering domains.
- Knowledge of software, hardware or physical engineering, and machine learning development life cycles.
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, and develop synthetic task generation and reinforcement learning environments.
- Partner with modeling leads to build customer-centric benchmarks and simulation or rule verifiers that grade executability, physical well-formedness, and specification satisfaction.
- Manage project planning using lightweight methodologies to improve research velocity and release predictability, and align cross-functional dependencies.
- Evaluate technical trade-offs involving geometric plausibility versus functional engineering correctness, manufacturability constraints, and reward-hacking risks.
- Manage project schedules, identify risks, and communicate with project stakeholders.
- Identify future opportunities, improve processes, and drive the technical direction of programs.
Benefits
- 20% bonus target
- Equity
- Benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
