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

Google•Mountain View, CA

11d•$192,000 - $278,000

About The Position

This Technical Program Manager role is within the Gemini Post-Training team in the DeepMind GenAI unit, which researches and enables new capabilities across Gemini models. The team builds large language models end-to-end using supervised fine-tuning (SFT), reinforcement learning (RL), inference-time self-improvement, and other techniques. The role applies program management methodologies across post-training data, evaluation, harness, and RL initiatives, balancing rapid progress with risk to deliver improvements across Gemini. The program manager partners with researchers, engineers, product managers, and other stakeholders to define scope, goals, timelines, and resource requirements, and turns research priorities into training data, evaluations, and model milestones. The work enables subagent capabilities intended to improve quality and efficiency on complex, long-running coding, research, and knowledge-work tasks. Google Technical Program Managers lead complex, multidisciplinary engineering projects through their lifecycle, planning requirements, managing schedules, identifying risks, and communicating with stakeholders. They use technical and leadership expertise to manage programs, improve processes, identify opportunities, and help drive technical direction.

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 program management experience driving delivery with research scientists and software engineers on foundation models, agentic systems, or RL pipelines.
Knowledge of software and machine learning development lifecycles, compute capacity planning, and experience managing complex, cross-functional dependencies.
Strong stakeholder management skills, including the ability to navigate ambiguity, influence outcomes, and align cross-functional teams without relying on direct authority.
Analytical depth in evaluating agentic trajectories, diagnosing failure modes, and translating insights into concrete data, harness, or RL interventions.

Responsibilities

Work with research and engineering teams to turn subagent initiatives spanning Supervised Fine-Tuning (SFT) distillation, Reinforcement Learning (RL), and dynamic harnesses into clear goals, milestones, and release plans.
Partner with modeling and evaluation teams to monitor evaluation leaderboards and track latency, token usage, parallelization speedups, and quality improvements.
Drive convergence between research training harnesses and production agent runtimes, managing cross-functional dependencies across data, evaluations, infrastructure, and compute resources.
Contribute to technical proposals by analyzing multi-agent trajectories, synchronous versus asynchronous delegation patterns, and overall performance headroom.
Manage end-to-end project planning using effective workflows, and translate complex modeling progress into clear, decision-ready updates for leadership and cross-functional audiences.