Research Scientist, User Signals from Agentic Logs, DeepMind

Design and implement mechanisms to harvest, filter, and utilize high-quality user signals (implicit and explicit) to iteratively improve Gemini models. Develop and refine reward models and critique models that accurately capture user intent and satisfaction, serving as the foundation for Reinforcement Learning from Human Feedback (RLHF) and other alignment techniques. Create and validate "horizontal" post-training recipes that can be robustly applied across different model sizes and modalities (including text and audio).

Investigate novel methods for weighting, synthesizing, and selecting data to maximize the impact of user signals on model performance. Establish rigorous evaluation frameworks to measure the correlation between offline metrics (reward model accuracy) and online user satisfaction.