AI Scientist

digiLab is a formative AI company with world-leading specialists in uncertainty quantification, applied where the stakes are highest and the margin for error is smallest.

Our customers are among the largest and most advanced engineering and science-led organisations in the world, and they're using our technology to tackle some of society's biggest challenges. How do we make fusion energy a commercial reality? Can we drive down the cost of renewable energy and accelerate the transition to a sustainable future? How do we support coral reef restoration, or strengthen Britain's national security? These are just a few of the critical questions our technology is helping to answer. 

At the heart of our work is digiLab's trustworthy and explainable AI platform, enabling our customers to accelerate innovation, reduce risk, and deliver real value through better informed decisions. 

We are looking for ambitious team members to help shape our pioneering technology and tackle some of the world's most impactful problems. 

Summary: 

The AI Scientist contributes to theLab’s scientific mission by supporting the development of foundational AI and machine learning capabilities. Working under the guidance of senior scientists, this role focuses on experimentation, modelling, and scientific exploration to advance innovative AI approaches.

This is a growth-oriented position designed to build technical depth, scientific judgement, and domain understanding through hands-on research and collaboration.

The Role: 

In this role, you will contribute to a range of research and technical activities aimed at strengthening digiLab’s AI capabilities and scientific output:

• Contribute to scientific research through experimentation, analysis, and implementation of modelling or analytical approaches.
• Develop and test innovative AI models, algorithms, and tools to address complex data-driven problems across domains such as nuclear fusion, environmental sustainability and healthcare.
• Engage with real-world problems as contexts for scientific inquiry, serving as technical lead for areas within your expertise to test, refine, and apply research ideas to achieve tangible impact.
• Collaborate with colleagues in theLab to explore new research directions and strengthen shared AI/ML capability.
• Develop high-quality, maintainable, and reproducible research artefacts, including code, experiments, analyses, and documentation.
• Follow best practices in software engineering, testing, and continuous integration to ensure research outputs are robust and reusable.
• Contribute to the research profile of theLab by publishing research outputs in academic journals, presenting work at conferences/industry events and making models available in open source repositories.
• Contribute to cross-functional teams to support the delivery of high-quality technical solutions where appropriate.
• Actively support the continuous improvement and innovation of the digiLab’s proprietary ML library.
• Participate in theLab’s intellectual culture through discussion, knowledge sharing, and scientific collaboration.
• Engage in ongoing professional development to strengthen technical skills, scientific reasoning, and domain expertise.
• Adopt AI tools to assist and accelerate your work.

Duties may evolve, and you may be asked to take on other reasonable responsibilities within your competence to support our growth.

Technical expertise and research standards:

We are looking for a capable scientist with strong mathematical foundations, a rigorous approach to research and the engineering ability to turn ideas into working ML methods.

Essential

• Masters or PhD qualification in a STEM field (engineering, physics, mathematics, computer science, data science, or a related field).
• A track record of research output you can point to: peer-reviewed publications, preprints, technical reports, patents, or substantial open-source contributions.
• Research experience beyond your taught degree — postdoctoral, industrial research, or an applied research role.
• Mathematical foundations: Solid understanding of probability, statistics, linear algebra and optimisation, with the ability to understand, implement and critically evaluate ML methods from the underlying mathematics.
• Scientific software engineering: Strong Python skills and experience with PyTorch or a similar framework, with the ability to implement algorithms from papers and write reliable, reproducible research code.
• Strong analytical and problem-solving skills.
• Clear written and verbal communication skills appropriate for scientific collaboration.
• Demonstrated curiosity and motivation to work on important, real-world problems.

Technical capabilities

We don't expect any one person to bring all of the following. You'll have hands-on experience in some of these areas and the motivation to build depth in others:

• Probabilistic machine learning: Understanding of probabilistic modelling, approximate inference and uncertainty quantification, and why predictive uncertainty and calibration matter.
• Deep learning and modern architectures: Working knowledge of contemporary neural architectures, how they are trained and where they fail.
• Experimental and empirical rigour: Understanding of experimental design, model evaluation and reproducibility, including baselines, ablation studies and investigating failure modes.

Particularly desirable

• Research experience in, or exposure to, Bayesian deep learning, physics-informed machine learning, uncertainty quantification, probabilistic numerical methods or approximate inference.
• Exposure to newer model architectures, such as foundation models, neural operators, geometric deep learning, recursive models or efficient specialist models.
• Understanding of scientific machine learning, physical modelling, differential equations or computational mathematics.
• Experience developing efficient ML methods under computational, data or deployment constraints.
• Evidence of original work, such as publications, a thesis, open-source contributions or substantial technical projects. The quality of your contribution matters more than publication venue or count.

Location: 

This role is primarily based on-site at one of digiLab’s offices in Exeter, Bristol, or London.

As an ambitious, rapidly-growing start-up, we’re looking for proactive, adaptable people who thrive in a fast-paced environment. Our standard working hours are 9.00–5.30pm, Monday to Thursday, though some flexibility outside these hours may be required to meet business needs.

Our Culture and Values

At digiLab, we're building something we truly believe in, and we're looking for like-minded people. 

We foster a culture of innovation, trust, and collaboration. Our values include:

• Creativity & Agility: Encouraging innovation and flexibility in goal achievement.
• Trust & Responsibility: Supporting each other in taking calculated risks for bold innovation.
• Open & Honest Collaboration: Ensuring transparent communication and alignment.
• High-Performance Standards: Continuously challenging ourselves to excel in delivery.
• Value-Driven Work: Regularly assessing our contributions toward company goals.

Benefits:

We value enthusiasm and loyalty, and we’re committed to offering a great work-life balance. Along with the exciting challenges this role provides, we offer a range of benefits including:

• Competitive Salary
• Personal & professional development
• BUPA private health care (via salary sacrifice)
• Company Cashplan
• Cycle to work scheme
• Referral Program
• Regular social events such as pizza nights, kayaking, bouldering and bake offs

Equal Opportunities:
digiLab is an equal opportunity employer. We welcome applications from candidates of all backgrounds and are committed to ensuring our recruitment processes are fair, inclusive, and legally compliant. We take equality, dignity, and non-discrimination seriously. 

Final Note:
We aim to respond to every applicant, but due to high application volumes, we may not be able to respond individually. Thank you for your interest in joining the digiLab team. The information you provide will be stored and used in line with our Privacy Notice.