Ai Security Researcher

Commonai C.I.C.; Cambourne; On-site; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Competitive salary & pension

Professional development

Networking opportunities

Office near Cambridge train station

Collaborative environment

High impact role

Job summary

CommonAI CIC in Cambridge is seeking an AI Security Researcher with an attacker mindset to challenge how AI systems can be broken, misused or circumvented. You will support the High Assurance programme, focusing on accuracy, explainability and accountability of AI in regulated environments such as financial services.

You will track research, test attacks, threat-model systems, and help develop interpretable models while sharing findings with Members.

Qualifications

• Proven experience in security research, offensive security or adversarial machine learning (e.g. red-teaming, penetration testing, vulnerability research, or attacks on ML models).
• A strong understanding of how modern AI systems work, including how large language models are trained, deployed and served.
• Familiarity with attacks on AI systems (e.g. jailbreaks, prompt injection, data poisoning, backdoors, model extraction).
• Strong Python, and the ability to build your own tools and experiments.
• Excellent written communication: you can turn complex technical findings into clear briefings for technical and non-technical readers.

Responsibilities

• Track key research and developments relevant to the High Assurance programme, including attacks on AI models, defences against them, and the security of the systems that serve them.
• Report on what these developments mean for the programme and our Members, through written briefings and presentations.
• Review the programme's technical decisions and directions from an attacker's point of view, and propose stronger alternatives.
• Threat-model AI systems used in regulated settings, covering model weights, training data, inference serving, and the tools and agents built on them.
• Red-team the systems we build, within an agreed scope, and report findings through a clear disclosure process.
• Help develop interpretability methods for the simpler models produced by our frontier research, and test their limits, for example by trying to hide behaviour the methods should detect.
• Build internal expertise by sharing what you learn with colleagues and helping answer technical questions from our Members.

Skills

Security research

Offensive security

Adversarial ML

Python

Technical communication

Education

PhD in security or ML

Job description

About CommonAI CIC

CommonAI CIC is a non-profit membership organisation, founded on a belief in collaborative engineering for the safe and responsible development of foundational AI technologies. A place where AI startups, enterprises large and small, public sector bodies and academia can share resources and knowledge, to codevelop and grow businesses, fast.

We are led by experienced founders, investors and engineers who believe that collaborative engineering drives faster AI innovation and are backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems.

The Opportunity

We are seeking an AI Security Researcher with the mindset of an attacker: someone who enjoys finding out how systems can be broken, misused or circumvented. Our research team works on reducing the complexity of large language models while retaining their performance.

You will support our High Assurance programme, which works on the accuracy, explainability and accountability of AI systems used in regulated environments such as financial services. You will track the research that matters to the programme, challenge its technical decisions, and help test whether the simpler, more interpretable models emerging from our frontier research are easier to audit in practice.

What You'll Do

• Track key research and developments relevant to the High Assurance programme, including attacks on AI models, defences against them, and the security of the systems that serve them.
• Report on what these developments mean for the programme and our Members, through written briefings and presentations.
• Review the programme's technical decisions and directions from an attacker's point of view, and propose stronger alternatives.
• Threat-model AI systems used in regulated settings, covering model weights, training data, inference serving, and the tools and agents built on them.
• Red-team the systems we build, within an agreed scope, and report findings through a clear disclosure process.
• Help develop interpretability methods for the simpler models produced by our frontier research, and test their limits, for example by trying to hide behaviour the methods should detect.
• Build internal expertise by sharing what you learn with colleagues and helping answer technical questions from our Members.

Requirements

• Proven experience in security research, offensive security or adversarial machine learning (e.g. red-teaming, penetration testing, vulnerability research, or attacks on ML models).
• A strong understanding of how modern AI systems work, including how large language models are trained, deployed and served.
• Familiarity with attacks on AI systems (e.g. jailbreaks, prompt injection, data poisoning, backdoors, model extraction).
• Strong Python, and the ability to build your own tools and experiments.
• Excellent written communication: you can turn complex technical findings into clear briefings for technical and non-technical readers.
• The judgement and confidence to challenge technical decisions constructively.
• A strong enthusiasm for using LLMs, coding assistants, and agents as core tools in your own software development process.

We also value:

• Experience with interpretability methods (e.g. probing, sparse autoencoders, circuit analysis), or a strong interest in learning them.
• Experience working on software systems that operate in highly regulated or high-assurance environments (e.g. financial services).
• An understanding of the latest AI safety research and active involvement in that community.
• Knowledge of hardware and side-channel security (e.g. timing attacks, risks in shared GPU and inference infrastructure).
• Familiarity with AI assurance standards and regulation (e.g. ISO/IEC 42001, NIST AI RMF, the EU AI Act).
• A public track record, such as published research, CVEs, conference talks, bug bounties or CTF results.
• A PhD in security, machine learning or a related field, or equivalent research experience.

Benefits

• A collaborative and supportive work environment
• The opportunity to have a high impact in a growing organisation
• Competitive salary package and pension
• Professional development opportunities
• Networking opportunities with influential people from across the tech sector, financial services, and academia
• A vibrant office environment located a few minutes' walk away from Cambridge train station

CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace.