Applications are invited for a Research Software Engineer or Senior Research Software Engineer to join Professor Ke Li's AI for Biology Group in the Research Faculty of the Earlham Institute, based in Norwich, UK.
Background:
The AI for Biology Group will be newly established at the Earlham Institute (EI), building on Professor Ke Li's established and well-funded research programme in fundamental AI, biological foundation models, AI co-scientists and automated scientific discovery. At EI, the group will develop a Generative Digital Biology (GDB) programme: experimentally grounded AI systems that learn from biological data, support hypothesis generation, guide experimental design and accelerate discovery.
The role:
We are seeking a technically strong, research-focused Research Software Engineer to design and build the AI-agent platform for the programme. You will connect AI models, scientific tools, biological datasets, experimental-design methods, compute infrastructure, laboratory automation and human decision-making into usable systems for AI-driven biological discovery.
The work will include AI Co-Scientist architecture, agent orchestration, tool registries, model and data interfaces, workflow execution, versioning and provenance, robust evaluation, permissioning, sandboxing and safe tool execution. You will develop reusable agentic workflows and an extensible demonstrator for scientific reasoning, hypothesis generation, experimental planning, result interpretation and human-AI interaction.
Biology and laboratory-automation experience are desirable but not essential; we welcome candidates from AI systems, research software engineering, robotics, scientific computing and related fields. The post offers an opportunity to release high-quality research software, contribute to high-impact research and support future funding applications.
Ideal candidate:
The successful candidate will have a PhD, or equivalent research or industrial experience at a comparable level, in Computer Science, AI, Robotics, Machine Learning, Software Engineering, Scientific Computing, Data Science, Computational Biology or a closely related quantitative discipline.
They will demonstrate strong hands-on software engineering ability and experience developing complex research software, AI systems, agentic workflows, scientific platforms, robotics/automation software or data-intensive infrastructure. They should be able to build robust and reproducible software using appropriate version control, testing, documentation, containers, APIs, databases, workflow tools and CI/CD, with deployment experience on HPC, GPU, cloud or distributed-computing environments.
Additional information:
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