(Alignment) Research Engineer/Research Scientist - Red Team

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.

Team Description

Risks from misaligned AI systems will grow in importance as AI systems become more capable, autonomous, and integrated into society. Understanding these risks and stress-testing mitigations is essential to ensuring advanced AI systems are developed and deployed safely and beneficially in the future. The Alignment Red Team is a specialised sub-team within AISI's wider Red Team focused on detecting and evaluating misalignment in frontier AI systems. We perform novel research to develop techniques for finding misalignment, and pre- and post-deployment evaluations of frontier AI systems to understand loss‑of‑control risks associated with models, such as deceptive alignment, research sabotage, and self‑exfiltration attempts. We share our findings with frontier AI companies, the UK and allied governments, to inform their respective deployments, research, and policy‑making. We also work directly with safety teams at frontier labs, sharing our evaluation findings to help improve their model alignment training and monitoring methodology.

Responsibilities

• Researching methods to automatically search for misalignment in frontier models, including misalignment related to loss‑of‑control risks such as research sabotage and self‑exfiltration.
• Building and running alignment evaluations relevant for loss‑of‑control risks that current benchmarks don’t capture, such as research and decision sabotage, power‑seeking behaviour and deception.
• Running pre‑deployment evaluations to test the alignment of AI systems, and analysing and reporting results to frontier AI companies and UK and allied governments.
• Contributing to public‑facing research publications (like our published alignment evaluation case study) and technical reports that advance the field's understanding of alignment risks.
• Designing and building software and tooling, including open‑source software, for better alignment evaluations, improving efficiency, realism, and usability.

The work could also involve:

• Conducting threat modelling, analysis, and conceptual thinking to understand crucial model behaviours that could lead to loss of control (e.g. AI research assistants at frontier labs), translating abstract risk concepts into concrete, testable hypotheses.
• Coordinating and producing holistic assessments of loss‑of‑control risk from the deployment of AI systems, or analysis of such assessments by frontier AI companies.
• Mentoring and advising external collaborators and researchers to do work relevant to the team’s goals and alignment testing more broadly.

What We’re Looking For

We’re seeking Research Engineers and Research Scientists to join our Alignment Red Team. We are open to hires at junior, senior, staff and principal research scientist/engineer levels.

• Ability to work autonomously on complex research projects involving substantial engineering.
• Have completed at least one substantial research project in AI safety, security or alignment involving substantial engineering, experiment design and analysis on frontier LLMs.
• Strong software engineering and ML experience writing complex projects involving language models and ML, beyond just research code.
• 1+ years professional experience programming in Python for ML or SWE work.
• Ability and experience writing clean, documented research code for machine learning experiments, including experience with ML frameworks like PyTorch or evaluation frameworks like Inspect.
• At least one substantial research or engineering project completed.
• Proven ability in a team environment – flexible, adaptive to needs, and willing to contribute wherever necessary.
• Impact‑driven mindset, motivated by doing the most important work rather than what’s superficially impressive.
• High velocity and high‑quality bar for outputs.

Highly Desirable

We don’t expect candidates to have all of these – they’re additional signals that help us identify exceptional fits for specific aspects of the role.

• Makes high‑quality decisions by identifying risks, and testing assumptions.
• Demonstrates this through strong prioritisation of research projects using clear, systematic criteria such as potential impact, feasibility, and the relative novelty of the research area.
• Familiarity with alignment literature, current methods for post‑training and aligning LLMs, loss‑of‑control risks and threat models, and the current state of the field.
• High‑quality research papers (first author at top ML venues such as NeurIPS, ICLR or ICML), particularly in relevant areas (such as AI safety, alignment, control, adversarial ML or evaluations).
• Professional experience working on alignment or evaluations, especially at frontier labs or other frontier third‑party evaluators.
• Strong open‑source software projects, particularly related to LLMs.
• Proficient usage of LLM coding tools and agents.

What We Offer

• Impact you couldn't have anywhere else.
• Incredibly talented, mission‑driven and supportive colleagues.
• Direct influence on how frontier AI is governed and deployed globally.
• Work with the Prime Minister’s AI Advisor and leading AI companies.
• Opportunity to shape the first & best‑resourced public‑interest research team focused on AI security.

Resources & access

• Pre‑release access to multiple frontier models and ample compute.
• Extensive operational support so you can focus on research and ship quickly.
• Work with experts across national security, policy, AI research and adjacent sciences.
• If you’re talented and driven, you’ll own important problems early.
• 5 days off and annual stipends for learning and development, and funding for conferences and external collaborations.
• Freedom to pursue research bets without product pressure.
• Opportunities to publish and collaborate externally.

Life & family

• Modern central London office (cafes, food court, gym), or where applicable, option to work in similar government offices in Birmingham, Cardiff, Darlington, Edinburgh, Salford or Bristol.
• Hybrid working, flexibility for occasional remote work abroad and stipends for work‑from‑home equipment.
• At least 25 days’ annual leave, 8 public holidays, extra team‑wide breaks and 3 days off for volunteering.
• Generous paid parental leave (36 weeks of UK statutory leave shared between parents + 3 extra paid weeks + option for additional unpaid time).
• On top of your salary, we contribute 28.97% of your base salary to your pension.
• Discounts and benefits for cycling to work, donations and retail/gyms.

Selection process

The interview process may vary candidate to candidate, however, you should expect a typical process to include some technical proficiency tests, discussions with a cross‑section of our team at AISI (including non‑technical staff), conversations with your team lead. The process will culminate in a conversation with members of the senior leadership team here at AISI. Candidates should expect to go through some or all of the following stages once an application has been submitted:

• Initial assessment
• Initial screening call
• Research interview
• Technical assessment
• Behavioural interview
• Final interview with members of the senior leadership team

Use of AI in Applications

Artificial Intelligence can be a useful tool to support your application, however, all examples and statements provided must be truthful, factually accurate and taken directly from your own experience. Where plagiarism has been identified (presenting the ideas and experiences of others, or generated by artificial intelligence, as your own) applications may be withdrawn and internal candidates may be subject to disciplinary action.

Internal Fraud Database

The Internal Fraud function of the Fraud, Error, Debt and Grants Function at the Cabinet Office processes details of civil servants who have been dismissed for committing internal fraud, or who would have been dismissed had they not resigned. The Cabinet Office receives the details from participating government organisations of civil servants who have been dismissed, or who would have been dismissed had they not resigned, for internal fraud. In instances such as this, civil servants are then banned for 5 years from further employment in the civil service. The Cabinet Office then processes this data and discloses a limited dataset back to DLUHC as a participating government organisation. DLUHC then carry out the pre‑employment checks so as to detect instances where known fraudsters are attempting to reapply for roles in the civil service. In this way, the policy is ensured and the repetition of internal fraud is prevented.

For more information please see -Internal Fraud Register.

The Civil Service Code sets out the standards of behaviour expected of civil servants. We recruit by merit on the basis of fair and open competition, as outlined in the Civil Service Commission's recruitment principles. The Civil Service embraces diversity and promotes equal opportunities. As such, we run a Disability Confident Scheme (DCS) for candidates with disabilities who meet the minimum selection criteria. The Civil Service also offers a Redeployment Interview Scheme to civil servants who are at risk of redundancy, and who meet the minimum requirements for the advertised vacancy.