About the Role
As a Research Scientist working on Safety Post-Training, you will develop and apply post-training methods and interpretability techniques to make frontier AI systems safer and better understood by researchers and policymakers. This role involves tackling hard problems in agent robustness, AI control protocols, and AI risk evaluations, collaborating across industry, the public sector, and academia, and regularly publishing findings.
Responsibilities
- Design and run post-training pipelines to study how training choices affect model safety, robustness, and alignment properties.
- Develop interpretability-informed evaluations that reveal how and why models produce unsafe, deceptive, or otherwise undesirable behaviors, and use those insights to guide targeted mitigations.
- Collaborate with policymakers, engineers, and other researchers to translate post-training and interpretability findings into actionable safety standards, evaluation benchmarks, and best practices.
Requirements
- Commitment to promoting safe, secure, and trustworthy AI deployments as frontier AI capabilities advance.
- Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches.
- A track record of published research in machine learning, particularly in generative AI.
- At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.
- Strong written and verbal communication skills to operate in a cross-functional team.
Nice to Have
- Experience with mechanistic interpretability, probing, or other techniques for understanding model internals.
- Familiarity with red-teaming or adversarial evaluation of post-trained models.
- Experience studying failure modes introduced or masked by post-training, such as reward hacking, sycophancy, or alignment faking.