Misuse Red Team - Research Engineer/Research Scientist

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 deadline for applying to this role is 30th September 2026, end of day, anywhere on Earth.

Team Description

Interventions that secure a system from abuse by bad actors or misaligned AI systems will grow in importance as AI systems become more capable, autonomous, and integrated into society.

The Misuse Red Team is a specialised subteam within AISI's wider Red Team. We red-team frontier AI safeguards for dangerous capabilities, research novel attack vectors, and develop advanced automated attack tooling. We share our findings with frontier AI companies (including Anthropic, OpenAI, DeepMind), key UK officials, and other governments to inform their respective deployment, research, and policy decision-making.

We have published on several topics, including novel automated attack algorithms (Boundary Point Jailbreaking), poisoning attacks, safeguards safety cases, defending finetuning APIs, third-party attacks on agents, agent misuse, and pre-training data filtering. Some example impact cases have been advancing the benchmarking of agent misuse, identifying novel vulnerabilities and collaborating with frontier labs to mitigate them, and producing insights into the feasibility and effectiveness of attacks and defences in data poisoning and fine-tuning APIs.

Role Description

We’re looking for research scientists and research engineers for our misuse sub-team with expertise developing and analysing attacks and protections for systems based on large language models or who have broader experience with frontier LLM research and development. An ideal candidate would have a strong track record of performing and publishing novel and impactful research in these or other areas of LLM research. We’re looking for:

• Research Scientists, who typically lead technical direction –pickingthe questions, designingthe experiments, and owningthe conclusions (typicallyevidencedby a strong publication record).
• Research Engineers, who typically lead execution – building the systems and code that make thoseexperimentspossible at scale, and owningreliability, speed, and reproducibility.

In practice, wecan support staff’s work spanning or alternating between research and engineering. If you have a preference, please specify this in your application.

The team is currently led by Eric Winsor and Xander Davies. You’ll work with incredible technical staff across AISI, including alumni from Anthropic, OpenAI, DeepMind, and top universities. You may also collaborate with external teams from Anthropic, OpenAI, and Gray Swan.

We are open to hires at junior, senior, staff and principal research scientist levels.

Representative projects you might work on

• Designing, building,runningand evaluating methods to automatically attack and evaluate safeguards, such as LLM-automated attacking and direct optimisation approaches.

• Building a benchmark for asynchronous monitoring for signs of misuse and jailbreak development across multiple model interactions.

• Investigating novel attacks and defences for data poisoning LLMs with backdoors or other attacker goals.

• Performing adversarial testing of frontier AI system safeguards and producingreports that are impactful and action-guiding for safeguard developers.

What we’re looking for

The experiences listed below should be interpreted as examples of the expertise we're looking for, as opposed to a list of everything we expect to find in one applicant:

You may be a good fit if you have:

• Hands‑on research experience with large language models (LLMs) - such as training, fine‑tuning, evaluation, or safety research.

• A demonstratedtrack recordof peer-reviewed publications in top‑tier ML conferences or journals.

• Ability and experience writing clean, documented research code for machine learning experiments including experience with ML frameworks likePyTorchor evaluation frameworks like Inspect.

• A sense of mission to the future? .

• Strong candidates may also have:

• Experience working on adversarial robustness, other areas of AI security, or red team-refence against any kind of system.

• Experience working on AI alignment or AI control.

• Extensive experience writing production quality code.

• Desire to and experience with improving our team through mentoring and feedback.
• Experience designing, shipping, and maintaining complex technical products.

Selection process

The interview process may vary candidate to candidate, however you should expect a typical process to include some technical&nbs