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

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

Percentage

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.

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, perturbation data, synthetic biology, engineering biology, drug discovery or experimental biology workflows

Desirable

Relevant Experience

Requirement

Importance

Experience turning open-ended research ideas into robust working systems, prototypes, platforms or demonstrators

Essential

Evidence of research-active technical contribution and high-quality outputs commensurate with career stage, such as open-source software, widely used research tools, benchmarks, technical reports, prototypes, patents, demonstrators or publications

Essential

Experience taking end-to-end ownership of complex technical work and delivering robust systems with limited supervision while collaborating across disciplines (essential for SC5)

Desirable

Evidence of technical leadership of a substantial research-software or AI-systems project or workstream, including architecture decisions, planning, integration and management of technical risk (essential for SC5)

Desirable

Clear evidence of leading major software or research outputs, such as a substantial open-source release, widely used research tool, production-quality platform, demonstrator, patent, technical report or publication (essential for SC5)

Desirable

Experience providing technical guidance or mentoring to junior colleagues, coordinating contributors, and making substantive contributions to grant applications or platform strategy (essential for SC5)

Desirable

Interpersonal & Communication Skills

Requirement

Importance

Excellent written and verbal communication skills, including the ability to explain complex AI systems and software design decisions to interdisciplinary collaborators

Essential

Ability to work collaboratively in an interdisciplinary team spanning AI, computer science, research software engineering, robotics, genomics, engineering biology and experimental biology

Essential

Ability to work independently, use initiative, solve complex research problems and deliver against agreed milestones

Essential

Additional Requirements

Requirement

Importance

Attention to detail

Essential

Promotes equality and values diversity

Essential

Commitment to reproducible, open and responsible research software

Essential

Able to present a positive image of self and the Institute, promoting both the international reputation and public engagement aims of the Institute

Essential

Willingness to work outside standard working hours when required

Essential

Willingness to undertake occasional national or international travel for collaborations and conferences

Essential

Willingness to embrace the expected values and behaviours of all staff at the Institute, ensuring it is a great place to work

Essential