(Senior) Research Software Engineer - AI Co-Scientist Systems
Job Title (Senior) Research Software Engineer - AI Co-Scientist Systems Post Number Closing Date 20 Aug 2026 Grade SC6/SC5 Starting Salary Salary: £38,000-£52,560
Hours per week 37 Project Title Generative Digital Biology: AI Co-Scientist Systems and Lab Automation Expected/Ideal Start Date 07 Sep 2026 Months Duration 36
Job Description Main Purpose of the Job 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. Key internal relationships are expected to include Earlham Biofoundry and engineering biology colleagues for lab automation, robotics interfaces and design build test learn workflows; Research e Infrastructure for HPC/GPU/cloud, reproducible software and scalable model deployment; BioFAIR, ELIXIR UK and Open and FAIR Data colleagues for AI ready resources, provenance, data/model documentation and interoperable workflows; Cellular Genomics and Single cell and Spatial Analysis for biological use cases; and relevant EI groups working on plants, microbes, biodiversity, health, genomics and data intensive bioscience.
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. External collaborations may include academic, public sector, infrastructure, clinical and industry partners where appropriate.
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.
- For appointment at SC5, take technical and operational leadership of a defined AI Co Scientist platform or workstream; own the architecture, roadmap, milestones and technical risk decisions; and deliver the work with limited supervision (essential for SC5).
- 20 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.
- 15 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.
- For appointment at SC5, coordinate cross platform integration, define interface contracts and engineering standards, resolve technical dependencies, and ensure coherent delivery across AI, data, infrastructure and automation partners (essential for SC5).
- 15 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.
- 15 Build an interactive AI Co Scientist demonstrator environment for research, collaboration, grant development and institutional showcase purposes.
- 10 Implement robustness, safety, permissioning, sandboxing, audit trails, provenance, reproducibility, monitoring and responsible use/dual use aware controls for AI systems operating in biological contexts.
- For appointment at SC5, define and lead implementation of software quality, testing, security, provenance, auditability and safe tool execution standards, including design/code review and release readiness decisions (essential for SC5).
- 10 Contribute intellectually to research papers, conference submissions, technical reports, open source software releases, demonstrations, presentations and community resources.
- For appointment at SC5, lead major software releases, demonstrators and technical/research outputs, and represent the work in relevant internal and external forums (essential for SC5).
- 10 Contribute to collaborative projects, competitive grant applications, documentation, testing, responsible data handling and long term platform strategy for the AI for Biology Group.
- For appointment at SC5, make substantive contributions to grant development and long term platform strategy, and provide technical guidance or mentoring to junior researchers or engineers (essential for SC5).
- 5 As agreed with line manager, any other duties commensurate with the nature of the role.
Person Profile Education & Qualifications Requirement Importance 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 Essential
Specialist Knowledge & Skills Requirement Importance 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 Essential Experience designing, implementing and maintaining complex research software systems, AI systems, agentic workflows, robotics/automation software, scientific platforms or data intensive computational infrastructure Essential Experience with modern AI systems or scientific computing systems, such as large language models, tool-using agents, retrieval augmented generation, workflow orchestration, model APIs, machine learning frameworks, and the ability to develop agentic workflows for scientific applications Essential Understanding of, and commitment to, trustworthy AI, robustness, safety, provenance, auditability, sandboxing, permissioning, monitoring or dual use aware AI workflows for biological applications Essential 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 Essential Demonstrable practical experience designing and building AI-agent, multi agent or scientific agent systems, including relevant experience in orchestration, tool use and evaluation (essential for SC5) Desirable 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 Desirable Experience with biological data, genomics, single cell data, imaging . click apply for full job details