Research Engineer – Human Influence

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. 

We are looking for a Research Engineer to join the Human Influence team. Successful candidates will be strong researchers and engineers with a track record of carrying out scalable work in LLM post-training and fine-tuning, especially with Reinforcement Learning; or with comparable expertise in engineering and validating large-scale evaluation pipelines. 

Projects the Research Engineer might deliver include:

• Designing and building a Reinforcement Learning environment aimed at mitigating a model’s ability to e.g. deceive a user in a one-to-one conversation or within multi-agent threads. 
• Leveraging state-of-the-art interpretability methods to identify why models exhibit concerning behaviour, and designing mitigations that can be applied to models irrespective of training regime. 
• Building the scalable system architecture underpinning the repeatable delivery and analysis of model evaluations and benchmarks. 
• 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 multidisciplinary team, and successful candidates come from a wide range of backgrounds. 

General:

• Proven experience deploying a benchmark, evaluation, or product to users, e.g. an evaluations pipeline in an app, an open-source contribution, or similar large-scale contributions to research. 
• Clear understanding of the current AI safety literature, and an interest in topics relevant to Human Influence. 
• Clear and consistent communication. 
• Clear understanding of fundamental Machine Learning concepts. 

Research and engineering: 

• Experience fine-tuning or post-training LLMs using standard methods, using common libraries like PyTorch, Keras, JAX, or custom code. 
  
• Comfortable working with RL environments and using RL or other reward-based methods to post-train or finetune an ML model, ideally an LLM.
• Writing scalable and maintainable production code in (at least) Python. 
• Comfortable with serving, scaling, and containerising ML code, e.g. using Docker, Kubernetes, Ray, FastAPI, SLURM, especially on large compute clusters. 

• Good understanding of model internals, e.g. for mechanistic interpretability research, or analysing model activations and weights 
• 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 and node.js deployments 

We will review applications as they come in, so would encourage you to apply early. If you are interested in this role and have engineering leadership experience, you may also want to consider our open Engineering Lead position.