About the job
As a Senior Research Scientist on our Reward Models team, you'll lead research efforts to improve how we specify and learn human preferences at scale. Your work will directly shape how our models understand and optimize for what humans actually want — enabling Claude to be more useful, more reliable, and better aligned with human values.
Responsibilities
Lead research on novel reward model architectures and training approaches for RLHF
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
Qualifications
Minimum
Have a track record of research contributions in reward modeling, RLHF, or closely related areas of machine learning
Have experience training and evaluating reward models for large language models
Are comfortable designing and running large-scale experiments with significant computational resources
Can work effectively across research and engineering, iterating quickly while maintaining scientific rigor
Enjoy collaborative research and can communicate complex ideas clearly to diverse audiences
Care deeply about building AI systems that are both highly capable and safe
Preferred
Have published research on reward modeling, preference learning, or RLHF
Have experience with LLM-as-judge approaches, including calibration and reliability challenges
Have worked on reward hacking, specification gaming, or related robustness problems
Have experience with constitutional AI, debate, or other scalable oversight approaches
Have contributed to production ML systems at scale
Have familiarity with interpretability techniques as applied to understanding reward model behavior