Senior Research Scientist, Reward Models

Lead research on novel reward model architectures and RLHF training approaches for large language models.
Develop and evaluate LLM-based grading and evaluation methods, including rubric-driven approaches that improve consistency and interpretability.
Research techniques to detect, characterize, and mitigate reward hacking and specification gaming.
Design experiments to understand reward model generalization, robustness, and failure modes.
Collaborate with the Finetuning team to translate research insights into improvements for production training pipelines.
Contribute to research publications, blog posts, and internal documentation; mentor other researchers and help build institutional knowledge around reward modeling.