AISI's Chem Bio (CB) team conducts technical research to assess evolving AI capabilities related to science R&D and CB misuse, and the effectiveness of technical safeguards that might mitigate risks arising from those capabilities.
The goal of our research is to inform critical decisions on security, opportunities, policy, and risk mitigation made by governments and AI developers.
We're a close-knit, unusually interdisciplinary team—made up of machine learning researchers and engineers, software engineers, virologists and bacteriologists, behavioural research scientists, biosecurity experts, long-standing CB policy specialists and talented generalists—who work closely with other technical and policy teams across government.
Over the next twelve months, CB will hugely scale the range and complexity of the evaluations and research programmes it carries out, and engage more deeply with partners in major AI labs, the wider biotech and pharma ecosystem and security services than it ever has before.
AI capabilities in the life sciences are advancing faster than at any point in history. Foundation models can now design novel proteins and interpret genomic sequences. Specialised biological models can both identify drug targets and design the compound to target them. These are extraordinary tools for scientific progress but also have the potential for harm if misused.
This role is for a technical researcher who can contribute strong ML and computational biology expertise to that mission. You will sit within a group of research scientists, subject matter experts and engineers, leading empirical research into the risk-relevant capabilities of specialised biological models, including biomolecular structure and generative-design systems. You will translate ambiguous questions about what these models could enable into rigorous research questions and experimental designs, assess whether in-silico performance translates into meaningful experimental outcomes, and investigate whether technical safeguards can reliably limit potentially dangerous capabilities. It is a role at the interface of machine learning, computational biology and biosecurity: shaping which capabilities we investigate, how we measure their real-world significance, and how we translate our findings into decisions by government and other trusted partners.
