Technical Program Manager, Gemini Post-Training, Subagents, DeepMind

Job description

Minimum qualifications

Bachelor's degree in a technical field or equivalent practical experience. 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.

This role sits within the Gemini Post-Training team in the DeepMind GenAI unit, which researches and enables new capabilities across our Gemini models. Our team builds large language models end-to-end through SFT, RL, inference-time self-improvement and many other techniques. As a Technical Program Manager for Gemini Post-Training (Subagents), you will apply program management methodologies across multiple post-training data, evaluation, harness, and RL initiatives, balancing rapid progress and risk to deliver improvements across Gemini.

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. Ultimately, you will enable subagent capabilities across Gemini Post-Training to improve both quality and efficiency across complex, long-running coding, research, and knowledge-work tasks.