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About the Role

As a Senior Research Scientist, you will lead the development of cutting-edge safety evaluations to quantify AI risks in critical domains like CBRN and Cyber. You will drive original research that advances safety methodology while collaborating with delivery teams building evaluations and red-teaming for frontier labs. This is a high-agency opportunity to conduct technical AI safety research that produces scientific outputs shaping the future of safe real-world AI deployment.

Responsibilities

Leading the development of novel safety evaluations in high-impact domains such as CBRN and Cyber to quantify emerging risks.
Executing original technical research in AI safety evaluation methods, taking ideas from concept to publication.
Shaping the R&D agenda by identifying strategic opportunities to advance safety evaluation methodology across Faculty and the broader ecosystem.
Contributing thought leadership and deep technical expertise to client delivery projects, evaluation work, and red-teaming for frontier labs.
Representing Faculty’s scientific leadership through active external engagement with the global research community, frontier labs, and government stakeholders.

Requirements

A track record of owning research end-to-end—from identifying novel problems to publication—driven by scientific curiosity and tenacity.
Hands-on experience designing and building AI evaluations or benchmarks, alongside a strong ability to reason about construct validity and mitigate confounds.
Expertise in red-teaming, adversarial testing, jailbreaking, indirect prompt injection, and assessing the robustness of model safeguards.
Strong foundational skills in experimental design, statistical analysis, and uncertainty quantification, including Bayesian methods.
Deep knowledge of language models, generative AI architectures, training methodologies, and safety mitigation techniques.
Solid Python proficiency combined with the engineering discipline required to build robust, reproducible research.
Experience in threat and risk modelling.
Background or knowledge in high-risk domains such as CBRN or Cybersecurity.
A history of high-impact AI research, evidenced by top-tier publications or equivalent practical achievements.

Skills

AI safety
AI risk quantification
CBRN
Cybersecurity
AI safety evaluation methods
Experimental design
Statistical analysis
Uncertainty quantification
Bayesian methods
Language models
Generative AI architectures
Safety mitigation techniques
Python
Threat modelling
Risk modelling

Location

Hybrid Working

Work Type

Hybrid Working
Part-time hours

Experience Level

Senior

Benefits

Unlimited Annual Leave Policy
Private healthcare and dental
Enhanced parental leave
Family-Friendly Flexibility & Flexible working
Sanctus Coaching

About the Company

Faculty was established in 2014 with the belief that AI would be the most important technology of our time.
Faculty has worked with over 350 global customers to transform their performance through human-centric AI.
Faculty innovates, builds, and deploys responsible AI.
Faculty brings unparalleled depth of technical, product, and delivery expertise to clients across government, finance, retail, energy, life sciences, and defence.
The company's business and reputation are growing rapidly.
Faculty is looking for individuals with intellectual curiosity and a desire to build a positive legacy through technology.
Faculty is a company where employees are empowered to envision and implement the most powerful applications of AI.
The central research and development team focuses on fundamental and applied technical AI safety research.
The team produces rigorous scientific outputs—publications, tooling, technical reports & evaluations—that advance the theory, practice, and understanding of AI risks.
The team's work directly informs frontier AI labs, government agencies, and national security institutes.
Research spans fundamental research using black-box and white-box approaches to understand and steer AI systems, to building and advancing safety evaluations.
The team cares deeply about mechanistic understanding and scientific rigor in measuring risks in AI systems.
Current research threads include uncertainty calibration, goal drift, misinformation mitigation, steering vectors, robust safeguard measurement, and the science of evaluations.
The team collaborates closely with Faculty's wider AI safety team, which has a track record in capability evaluations and red-teaming for misuse risk across CBRN, cybersecurity, societal, and psychosocial harms.
This work has been conducted for leading frontier model developers and national safety institutes, and featured in model cards and safety reports from Anthropic, GDM, Meta & OpenAI.

Equal Opportunity

Faculty aims to grow the best team, not the most similar one, recognizing that diversity of individuals fosters diversity of thought and strengthens the principle of seeking truth.
Diverse teams deliver better work, relevant to the world.
The team is united by deep intellectual curiosity and a desire to use abilities for measurable positive impact.
Applications are strongly encouraged from people of all backgrounds, ethnicities, genders, religions, and sexual orientations.
If you don’t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles.