We are pushing the boundaries across multiple domains. Our global teams offer varied learning opportunities and career pathways for those driven to achieve exceptional results through collective effort.
Responsibilities
- 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.
Minimum qualifications:
- 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 one or more peer-reviewed machine learning or natural language processing (NLP) conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP).
Preferred qualifications:
- 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.