Research Engineer​/Research Scientist – Model Transparency

About the AI Security Institute

The AI Security Institute is the world's largest and best-funded team dedicated to understanding advanced AI risks and translating that knowledge into action. We’re in the heart of the UK government with direct lines to No. 10 (the Prime Minister's office), and we work with frontier developers and governments globally.

We’re here because governments are critical for advanced AI going well, and UK AISI is uniquely positioned to mobilise them. With our resources, unique agility and international influence, this is the best place to shape both AI development and government action.

The ability to effectively evaluate and monitor AI systems will grow in importance as models become more capable, autonomous, and integrated into society. If models can detect and game evaluations, obscure their reasoning, or behave differently under observation, the safety claims that governments and developers rely on become unreliable. Understanding and addressing these risks is essential to ensuring that oversight of advanced AI systems keeps pace with their capabilities.

The Model Transparency team is a research team within AISI focused on ensuring that evaluations, assessments, and monitoring of frontier AI systems remain reliable as models become less transparent. We research how and why oversight is declining – through phenomena such as evaluation awareness, unfaithful chain-of-thought reasoning, and changes in model architectures – and develop methods (including white and black box methods) to detect, measure, and mitigate potential issues.

We share our findings with frontier AI companies (including Anthropic, OpenAI, Deep Mind), UK government officials, and allied governments, and publicly to inform their deployment, research, and policy decisions. We also work directly with safety teams at frontier labs, contributing to safety case reviews and helping improve their alignment evaluation methodology.

Our recent work includes auditing games for sandbagging, reproducing natural emergent misalignment from reward hacking, and identifying open‑weight language models that game propensity evaluations.

We're looking for Research Scientists and Research Engineers for the Model Transparency team with expertise in technical AI safety – such as interpretability, capability or alignment evaluations, model transparency – or with broader experience with frontier LLM research and development. An ideal candidate would have a strong track record of high-quality research in technical AI safety or adjacent fields.

• Research Scientists
  , drive the technical substance of our work – staying abreast of the literature, proposing and designing experiments, conducting rigorous analyses, and owning the evidence stack from experiment through to written output. They write, critique, and strengthen the team's reports and publications.
• Research Engineers
  , build the systems and tooling that make our research possible and fast – scaling experimental workflows, automating processes, solving infrastructure challenges, and creating systems that accelerate the entire team's output.

We're interested in candidates along the spectrum between Research Engineers and Research Scientists. The application form will ask you to indicate which role you lean towards.

The team is led by Joseph Bloom, advised by Geoffrey Irving. You'll work with talented, mission‑driven technical staff across AISI, including alumni from Anthropic, OpenAI, Deep Mind, and top universities. You may also collaborate with external research teams including those at frontier AI labs, METR, and FAR.

We are open to hires across a range of experience levels.

This role requires three days a week in person, with flexibility for occasional periods of remote working.

• Developing a chain-of-thought monitorability benchmark and comparing monitorability properties across frontier AI systems, leveraging AISI’s unique access to reasoning traces from multiple labs.
• Designing and running experiments on open-weight models to study alignment and oversight‑relevant phenomena – such as reproducing emergent…