Partner with senior leads to architect closed-loop RSI pipelines that autonomously diagnose failure modes, refine instructions/trajectories, synthesize targeted training/eval data, and verify gains without manual intervention. Investigate and build automated multi-agent systems that audit, compress, and de-conflict complex model context—maximizing token efficiency and inference speed while preserving behavioral and safety guardrails.
Bridge inference-time context engineering with model post-training (SFT/RL) to distill complex rules into model weights. Advance automated optimization methods for production multimodal and reasoning systems. Help define the multi-quarter roadmap across Engineering and Research teams, establishing rigorous automated evaluation gates.
Job description provided by Google.
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