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DeepMind

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Technical Program Manager, Gemini Post-Training, Consumer Agents, DeepMind

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Summary

A Technical Program Manager at Google DeepMind coordinating Gemini post-training initiatives (SFT, RL, data, and evaluations), turning research and engineering work into milestones and release plans. The role blends LLM/ML lifecycle knowledge with end-to-end program delivery, cross-functional alignment, and leadership communication.

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.

This team is deeply experienced in building large language models end-to-end, with a distinct emphasis on post-training, evaluation, supervised fine-tuning, reinforcement learning techniques, and inference-time self-improvement. We are focused on building advanced-reasoning systems towards AGI, advancing model capabilities, and ensuring our generative models are aligned, factual, and highly capable.

As a Technical Program Manager for Gemini Post-Training, you will apply program management methodologies across multiple post-training data, evaluation, and modeling initiatives, balancing rapid progress and risk to deliver sustainable improvements across Gemini model releases

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: $163000 - $236000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.
  • Optimize post-training initiatives (Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), data, evaluations) into clear goals, milestones, and Gemini model release plans with research and engineering teams.
  • Manage end-to-end project delivery, improving velocity through lightweight methodologies while resolving technical and operational challenges across data and model workflows.
  • Align cross-functional dependencies across data pipelines, evaluation platforms, compute resources, and external partners to ensure timely Gemini releases.
  • Track progress, evaluate technical proposals, anticipate execution risks, and propose viable solutions to overcome blockers.
  • Translate complex technical progress into decision-ready updates for leadership, demonstrating a commitment to understanding LLMs and AGI research.

Minimum qualifications:

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

Preferred qualifications:

  • 8 years of program management experience, driving end-to-end delivery closely with research and engineering teams.
  • Familiarity with the LLM post-training lifecycle, including training data pipelines, SFT, RL, and model evaluation.
  • Knowledge of software and ML development lifecycles, and experience partnering closely with product management.
  • Skill in navigating ambiguity, adapting plans to changing research conditions, and influencing outcomes without direct authority.

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