About The Role

What You'll Be Doing

You’ll spend your time across two tracks of work:

Research

• How and what should we measure to understand the efficacy of control measures?

  How can we gather empirical evidence about how likely a monitor is to prevent harm — and what can we legitimately conclude from it? How do you estimate a monitor's recall against dangerous behaviours nobody has seen yet? These are difficult questions – conceptually and empirically. Day to day this looks like: designing and running ML experiments (including RL and other optimisation-heavy work), building the adversarial attacks that generate the evidence, writing arguments, and arguing them out with the rest of the team. We intend to publish this work.

Testing

• Running evaluations of frontier labs' monitors, and reporting the implications.

  Turning our research into concrete assessments of real systems: threat modelling how an AI attacker would actually operate in a frontier internal deployment; breaking monitors, sandboxes and the surrounding infrastructure; conducting security analyses; and producing reports that are decision-relevant and action-guiding for the companies and for government.

Underpinning Both

• Building tooling and experimental pipelines that let us go from question to result fast, at a quality bar that makes the results reusable.
• Heavy use of LLMs to automate our own attack, evaluation and analysis loops — and getting faster as models improve.
• Building and running the infrastructure for training and serving models at the scale our experiments need.

Research Scientists and Research Engineers

We're looking for research science and engineering skills, and we're excited to hear from strong scientists, strong engineers, and people who are a bit of both.

We’re deliberately open on seniority. For exceptional candidates, with experience leading research teams, we’ll grow the scope to match.

What We're Looking For

The experiences listed are examples of the expertise we're looking for, rather than a list of everything we expect to find in one applicant.

Essential Requirements

• Demonstrated ability to design, build and run ML experiments on frontier models, and to work autonomously on complex research projects involving substantial engineering. This includes black-box work (API-based evaluations and attacks) and ideally some white-box work (e.g. fine-tuning open-weight models).
• Strong software engineering and ML experience: writing clean, documented, reusable code for machine learning experiments — beyond one-off research scripts — including experience with LLM finetuning and inference frameworks, or evaluation frameworks like Inspect.
• The ability to understand and critique how an experiment does and does not support a safety claim – including an understanding of why AI safety and control are hard problems, or a clear appetite to get up to speed fast.
• Impact-driven mindset and a collaborative team player: motivated by the work that most reduces risk rather than what is superficially impressive, flexible about what needs doing, and high velocity with a high-quality bar for outputs.

Highly Desirable

We don't expect candidates to have all of these — they're additional signals that help us identify exceptional fits for specific aspects of the role.

• A good working model of frontier AI companies' internal deployments: what their ML infrastructure and dev practices look like, the kinds of experiments they run internally, and where the security weak points and easiest escape routes would be.
• An exceptional red-teaming mindset: instinctively finding the path a capable adversary would actually take, whether against a model, a monitor or a sandbox.
• Experience with ML optimisation: RL, SFT, evolutionary methods, or similar. Experience optimising hard against a defined metric and making (and justifying) careful measurement choices.
• Strong written communication and argumentation: high-quality research write-ups in any medium — a paper, a blog post, an internal report, an unusually good thread — where the reasoning, not just the result, is the point.
• Willingness and ability to construct and defend arguments for safety c