Engineering

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 22nd October 2026, end of day, anywhere on Earth. **

Team** Description

The Human Influence (HI) team focuses on the ways in which AI can influence human beliefs, decisions, and behaviour. A substantial class of AI risk operates through people. AI systems can persuade people to change their beliefs and to take action; can build trusting relationships with people in order to exploit them; can extract private information from them; and can hold delegated ownership of high-stakes decisions. 

Our work is highly interdisciplinary, drawing on methods from computational social science, AI safety and security, cognitive and behavioural science, machine learning, and data science. Typical projects include running rigorous human-AI interaction studies and randomised controlled trials, building evaluations and benchmarks that track AI capabilities across model releases, eliciting model capabilities through fine-tuning and self-play, and developing datasets to monitor real-world risk exposure and severity.

Role Description

We are looking for an Engineering Lead to manage and oversee a growing team of engineers. The team’s work includes: developing new evaluations and technical capability demonstrations, building robust data pipelines, driving world-leading research studies using complex multi-agent setups, finetuning LLMs (>100B parameters), and building custom Reinforcement Learning environments. 

Projects the Engineering Lead might lead or deliver as individual contributor include:

• Designing the scalable system architecture underpinning the repeatable delivery and analysis of model evaluations and benchmarks. 
• Building an automated pipeline that recruits participants and runs a human-AI experiment end-to-end, to track how a given capability shifts with each new model release. 
• Designing and building a toolkit for multi-agent studies, which allows researchers to run synthetic as well as human-in-the-loop studies seamlessly and with performance guarantees.