Work with research and engineering teams to optimize 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, tracking 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, sync vs. async 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.
Job description provided by Google.
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