Research Scientist (Biological Models), Chem-Bio

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 6th September 2026, end of day, anywhere on Earth.

About the team

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.

About the role

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.

What you will own

• Evaluate the risk-relevant capabilities of specialised biological models:Translate important but ambiguous questions about the capabilities of state-of-the-art biological models (including biomolecular structure and generative design models) into measurable research questions and experimental designs.
• Connect computational and experimental evidence: Use published experimental results, biological datasets, expert review and, where appropriate, external wet-lab collaborations to assess whether in-silico performance translates into experimentally relevant outcomes.
• Identify feasibility and effectiveness of technical safeguards:Lead research into the feasibility and effectiveness of technical safeguards for specialised biological models, including access controls, model-level interventions, monitoring, detection and capability-limiting approaches.
• Track the technical frontier: Identify important developments in biological AI and determine which new models, methods or capabilities AISI should investigate. Help shape the team’s research agenda as the field evolves.
• Communicate to decision-makers: Produce clear technical reports, briefings and recommendations for senior decision-makers within government and other trusted partners, translating a complex technical evidence base into actionable conclusions that inform wider cross-Government and industry efforts in this space.
• Collaborate across expert communities:Work closely with research scientists, engineers, biosecurity experts, policy teams, AI safety specialists and external scientific partners to ensure AISI’s evaluations are technically rigorous and policy-relevant.

Role requirements

• Deep, hands-on experience working with biological machine learning models. You have worked directly with biological AI models, such as protein design models, generative, structure prediction or scientific foundation models.
• Strong machine-learning background and applied engineering skills. You understand modern machine-learning methods and have practical experience training, fine-tuning, adapting or evaluating models using PyTorch or similar. You can write robust, readable and maintainable Python code.
• Computational biology expertise.You have sufficient understanding of molecular biology, biochemistry, structural biology, protein science, genomics or a related area to reason seriously about biological data, model outputs and the scientific validity of an evaluation.
• Strong empirical judgement. You know how to design experiments to answer pre-specified research questions, identify relevant baselines/controls for these experiments and critically interpret scientific results. You can identify limitations in public benchmarks, evidential gaps and cases where results are being over- or under-interpreted.
• Mission orientation. You are motivated to conduct technical research with direct public-interest and policy impact. You understand that work at the intersection of AI and biology may carry dual-use sensitivity and can operate with appropriate discretion.
• Collaborative interdisciplinary communication. You can explain complex findings and their uncertainty across audiences, from researchers, engineers, subject-matter experts, policy and security stakeholders, without blurring the bottom line, and work effectively with people from very different