About the role
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.
This role focuses on pushing the frontier of reward modeling for large language models. You'll develop novel architectures and training methodologies for RLHF, research new approaches to LLM-based evaluation and grading (including rubric-based methods), and investigate techniques to identify and mitigate reward hacking. You'll collaborate closely with teams across Anthropic, including Finetuning, Alignment Science, and our broader research organization, to ensure your work translates into concrete improvements in both model capabilities and safety.
We're looking for someone who can drive ambitious research agendas while also shipping practical improvements to production systems. You'll have the opportunity to work on some of the most important open problems in AI alignment, with access to frontier models and significant computational resources. Your work will directly advance the science of how we train AI systems to be both highly capable and safe.
Note: For this role, we conduct all interviews in Python.
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
You may be a good fit if you
- 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
Strong candidates may also
- 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
