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 30 th

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. W e

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

–

picking

the questions, designing

the experiments, and owning

the conclusions (typically

evidenced

by a strong publication record).

Research Engineers , who

typically lead

execution

– building the systems and code that make those

experiments

possible at scale, and owning

reliability, speed, and reproducibility.

In practice, we

can 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,

running

and 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 producing

reports 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 demonstrated

track record

of 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 like

PyTorch

or evaluation frameworks like Inspect.

A sense of mission, urgency, responsibility for success.

An ability to bring your own research ideas and work in a self-directed way, while also collaborating effectively and prioritizing team efforts over extensive solo work.

Strong candidates may also have:

Experience working on adversarial robustness, other areas of AI security, or red teaming 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