The deadline for applying to this role is Sunday 11th October 2026, end of day, anywhere on Earth. TeamDescription

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 Leadto 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 architectureunderpinning the repeatable delivery and analysis of model evaluationsand 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.
• Delivering ambitious, engineering-heavy research projects on Human Influence topics, for instance by leveraging post-training techniques on a large compute cluster.

This is a leadership role. Competitive candidates will be strong engineers with a track record of leading engineering projectsfrom scoping to production. The Human Influence Engineering Lead will work with Human Influence team leadership and senior AISI stakeholders to develop and deliver an engineering roadmap for the Human Influence team, and will lead and grow the HI Engineering function, including managing Research and Software Engineers.

Who we're looking for

This is a multidisciplinary team, and successful candidates come from a widerange of backgrounds.

Essential

General:

• Experienceleadinga complex engineering or research project from scoping to deployment or publication
• Clear understanding and views on how AI and coding assistants should change how an engineering team works and interacts with research teams
• Clear understanding of the current AI safety literature, and an interest in topics relevant to Human Influence
• Clear and consistent communication

Research and engineering:

• Experience translating research questions into reproducible engineering tasks
• Proven experience working with non-technical stakeholders to support a product or research roadmap, e.g., leading to a product with actual users, benchmarks and evaluations, or a research paper
• Clear grasp of software design concepts, especially for cloud deployments and databases, e.g. using Docker, Kubernetes,or AWS primitives– production experience preferred
• Writing scalable and maintainable production code in (at least) Python
• Experience building or finetuning an ML model, ideally an LLM, using common libraries like PyTorch, Keras, JAX, or custom code

Desirable

• Experience shipping an AI safety pipeline to production (e.g. evaluations, monitoring, serving custom models), going beyond research prototypes
• Experience using data to answer complex research questions, e.g. by scoping, training, and validating a classifier or finetuned LLM
• Experience with frontend (e.g. TypeScript) and node.js deployments
• Line management experience of at least 1 year
• Experience hiring and mentoring technical staff

We will review applications as they come in, so would encourage you to apply early. If you are interested in this role but do not yet have engineering leadership experience, you may want to considerour open Research Engineer position.