(Senior) Research Software Engineer — AI Co-Scientist Systems in Norwich

The post holder will take a research-active software engineering and AI systems role in the AI for Biology Group, building the AI-agent-driven platform required for Generative Digital Biology. The role will connect foundation models, scientific tools, biological datasets, experimental design algorithms, lab automation workflows, robotics interfaces and human‑in‑the‑loop scientific decision‑making.

This is a platform‑building research role, not a conventional bioinformatics software support post. The successful candidate will contribute intellectually to research, develop publishable AI systems, build open‑source software and demonstrators, co‑author research outputs and support competitive grant applications. The role would suit a highly capable computer scientist, AI systems researcher, robotics engineer or research‑active software engineer who wants to build the technical backbone for AI‑driven biological discovery.

A key objective will be to develop an AI co‑scientist demonstrator for the group and the Institute: a platform that can show how AI agents can interact with human scientists via virtual/augmented reality, reason over biological questions, call scientific tools and APIs, use foundation models, design experiments, interface with computational and physical workflows, and support rigorous, auditable and reproducible scientific discovery.

The post holder will work closely with the other AI for Biology appointments. Foundation model research in the group will provide biological representations and predictive models; generative and causal AI research will provide design and experimental‑decision algorithms; this RSE role will build the software, agentic workflows, tool registries, automation interfaces, provenance mechanisms and demonstrator environment that connect these components into a usable scientific discovery system.

This will be a highly collaborative role embedded across EI. The post holder will work with EI colleagues and platforms to connect AI‑designed hypotheses and candidates with biological data, experimental design and validation routes, including potential collaborations with Earlham Biofoundry and engineering biology colleagues on AI-guided design‑build‑test‑learn cycles; with Cellular Genomics and Single‑cell and Spatial Analysis on perturbation, cell‑state and single‑cell/spatial omics use cases; and with BioFAIR, ELIXIR‑UK, Open and FAIR Data and Research e‑Infrastructure colleagues on AI‑ready design datasets, benchmarks, provenance and reproducible workflows.

Prior experience in lab automation is desirable but not essential. We particularly welcome candidates with exceptional computer science, large‑scale machine learning systems, robotics or software engineering backgrounds who are motivated to apply their skills to biological discovery and to learn the relevant biological and automation context.

Key Relationships

• INTERNAL: Reporting to Professor Ke Li, the post holder will work closely with the AI for Biology Group and collaborate across EI’s research programmes, National Bioscience Research Infrastructures, Research e‑Infrastructure and technology platforms.
• EXTERNAL: The post holder will interact with UK and international collaborators in AI, AI agents, machine learning systems, research software engineering, robotics, laboratory automation, autonomous experimentation, computational biology, engineering biology, genomics, plant science, human health and therapeutic discovery.

Main Activities & Responsibilities

• Design and build the AI Co‑Scientist system/software architecture for the GDB programme, including agent orchestration, tool registries, model interfaces, workflow execution, data/model versioning and provenance tracking.
• Develop AI‑agent workflows for scientific reasoning, literature/data retrieval, tool use, planning, hypothesis generation, experimental design, result interpretation, iterative refinement and human‑in‑the‑loop decision‑making.
• Build software interfaces connecting AI agents with biological datasets, foundation models, generative models, Bayesian experimental design, optimisation algorithms, benchmarking tools and HPC/GPU/cloud resources.
• Develop interfaces to lab automation, robotics, biofoundry workflows, instrument‑control APIs, LIMS/ELN systems, digital‑twin/simulation environments or related physical AI workflows where appropriate.
• Build an interactive AI Co‑Scientist demonstrator environment for research, collaboration, grant development and institutional showcase purposes.
• Implement robustness, safety, permissioning, sandboxing, audit trails, provenance, reproducibility, monitoring and responsible‑use/dual‑use‑aware controls for AI systems operating in biological contexts.
• Contribute intellectually to research papers, conference submissions, technical reports, open‑source software releases, demonstrations, presentations and community resources.
• Contribute to collaborative projects, competitive grant applications, documentation, testing, responsible data handling and long‑term platform strategy for the AI for Biology Group.

Person Profile

• 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.
• Strong hands‑on software engineering ability, evidenced through substantial implemented systems, research software, open‑source code, AI/ML platforms, robotics/automation software or scientific computing projects.
• Experience designing, implementing and maintaining complex research software systems, AI systems, agentic workflows, robotics/automation software, scientific platforms or data‑intensive computational infrastructure.
• Understanding of, and commitment to, trustworthy AI, robustness, safety, provenance, auditability, sandboxing, permissioning, monitoring or dual‑use‑aware AI workflows for biological applications.
• Ability to build robust, maintainable and reproducible software using version control, testing, documentation, containers, APIs, databases, workflow tools, CI/CD and deployment on HPC, GPU, cloud or distributed computing environments.
• Demonstrable practical experience designing and building AI-agent, multi‑agent or scientific‑agent systems, including relevant experience in orchestration, tool use and evaluation.
• Experience with robotics, lab automation, self‑driving laboratories, autonomous experimentation, instrument control, liquid handling platforms, automated microscopy, microfluidics, sequencing workflows, biofoundry platforms, ROS/ROS2 or related hardware/software integration.
• Experience with biological data, genomics, single‑cell data, imaging, perturbation data, synthetic biology, engineering biology, drug discovery or experimental biology workflows.

Interpersonal & Communication Skills

• Excellent written and verbal communication skills, including the ability to explain complex AI systems and software design decisions to interdisciplinary collaborators.
• Ability to work collaboratively in an interdisciplinary team spanning AI, computer science, research software engineering, robotics, genomics, engineering biology and experimental biology.
• Ability to work independently, use initiative, solve complex research problems and deliver against agreed milestones.

Additional Requirements

• Attention to detail.
• Promotes equality and values diversity.
• Commitment to reproducible, open and responsible research software.
• Able to present a positive image of self and the Institute, promoting both the international reputation and public engagement aims of the Institute.
• Willingness to work outside standard working hours when required.
• Willingness to undertake occasional national or international travel for collaborations and conferences.
• Willingness to embrace the expected values and behaviours of all staff at the Institute, ensuring it is a great place to work.

Who We Are

The Earlham Institute harnesses the power of data‑driven biology to accelerate solutions for health, biodiversity, and food security. Based at Norwich Research Park, the Earlham Institute is one of eight institutes strategically funded by BBSRC.

Our science combines world‑class technology, interdisciplinary expertise, and training and development across genomics, engineering biology and data science, to decode the scale and complexity of living systems.

We believe we can achieve more if we work together. That's why we collaborate with the global science community and industry partners, while also inspiring the next generation of scientists and technical specialists.

The AI for Biology Group will be newly established at the Earlham Institute (EI), while building on Professor Ke Li’s established and well‑funded research programme in fundamental AI, biological foundation models and AI‑driven scientific discovery.

The successful candidate will join a group with both ambition and infrastructure. The group is supported by substantial AI compute for frontier model development, including more than 70 dedicated GPUs, currently comprising 12 NVIDIA H200 and 8 NVIDIA H100 GPUs, alongside further capacity through the Norwich Data Centre and routes to national‑scale AI compute such as Isambard‑AI.