Technical Program Manager, Gemini Post-Training, Knowledge Work, DeepMind

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.

Artificial Intelligence could be one of humanity’s most transformative inventions. We are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate product innovation. Our team is experienced in building large language models end-to-end, with a distinct emphasis on post-training, evaluation, supervised fine-tuning, reinforcement learning, and inference-time self-improvement.

In this role, 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 releases. You will partner with researchers, engineers, product managers, and cross-functional stakeholders to define project scope, goals, timelines, and resource requirements turning research priorities into high-quality training data, evaluations, and deliverable model milestones.

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.

Responsibilities

• Organize and optimize post-training initiatives (Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), data, and evaluations) into clear goals, milestones, and release plans with research and engineering teams.
• Manage end-to-end project planning and cross-functional dependencies across data pipelines, evaluation platforms, compute, and external partners to align with Gemini model releases.
• Track progress, maintain program documentation, and proactively resolve technical and operational blockers across data and model development workflows.
• Evaluate technical proposals, understanding trade-offs, and translate complex modeling progress into clear, decision-ready updates for leadership and cross-functional audiences.
• Apply lightweight program methodologies to improve velocity and predictability while fostering a deep understanding of post-training, LLMs, and AGI research.

Minimum qualifications:

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

Preferred qualifications:

• 8 years of program management experience, delivering end-to-end projects closely with research and engineering teams.
• Familiarity with the LLM post-training lifecycle, including training data pipelines, SFT, RL, and model evaluation.
• Proven skill navigating ambiguity and adapting strategies as research and project conditions change.
• Strong communication and stakeholder management skills to influence outcomes and align technical and product teams.