Two backgrounds are particularly well suited to this role: researchers with some experience in more fundamental interpretability who want to evaluate and refine those methods in realistic settings, including agentic coding, long contexts, and realistic threat models; and researchers from evaluations, AI control, red-teaming, or reinforcement learning who have begun working with model internals and want to deepen that work. In both cases, prior hands-on experience with white-box methods and a demonstrated interest in their practical application are preferred. (Note that this role is not well suited to researchers whose primary interest is interpretability as a foundational science.)
If you are new to AI Safety research, that is also ok! But please be prepared to explain how your previous research experience (e.g. in applied ML) could be leveraged for our work, and how you are engaging with the field of technical AI safety research today.
We are hiring across a range of levels of experience. You may already have some of the following, though not necessarily all.
- Hands-on experience applying at least one white-box method to a real model (activation explainers, SAEs, steering, attribution, influence functions, probes, or similar), and an informed view of its limitations.
- A track record in AI safety: a paper, a fellowship project, or substantive public writing. Alumni of programs such as MATS, Astra, Anthropic Fellows, SPAR, Pivotal, LASR or similar programs are especially encouraged to apply.
- Experience with evaluations, AI control, red-teaming, reinforcement learning, or post-training of LLMs. Experience running large training runs is a plus.
- The ability to communicate novel methods and results clearly to technical and non-technical audiences.
- A PhD or several years of research experience in computer science, machine learning, physics, statistics, or a related field.
- Previous experience in applied ML for other fields: e.g. biology, chemistry, materials science, robotics, etc.