Requirements

• PhD in Machine Learning, natural language processing, or a related field (or equivalent practical experience).
• Experience with software development using Python and one or more deep learning frameworks (e.g., PyTorch or JAX).
• Research experience in post-training, reinforcement learning (RL), agentic RL, preference learning, or reward modeling.
• First-author (or co-author) publication in peer-reviewed machine learning or natural language processing (NLP) conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP).

Nice to haves

• First-author publications at top ML/NLP venues (NeurIPS, ICML, ICLR, ACL, EMNLP).
• Experience with modern alignment and post-training methods (e.g., RLHF, DPO, PPO, rejection sampling, or self-play).
• Experience modeling multi-turn interaction data, agent trajectories/logs, or user feedback (implicit and explicit signals).
• Experience training or evaluating reward models, critique models, or automated evaluators (e.g., LLM-as-a-judge).
• Experience in agentic post-training.

What you'll be doing

• 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 capture user intent and satisfaction, supporting Reinforcement Learning from Human Feedback (RLHF) and other alignment techniques.
• Create and validate "horizontal" post-training recipes applicable across different model sizes and modalities (including text and audio).
• Investigate novel methods for weighting, synthesizing, and selecting data to maximize user signals' impact on model performance.
• Establish rigorous evaluation frameworks to measure correlations between offline metrics (reward model accuracy) and online user satisfaction.