Staff Scientist
digiLab · Bristol
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.
The Role:
In this role, you will shape theLab’s research trajectory and external scientific presence:
- Own and lead significant fields, programmes, or portfolios of scientific inquiry, shaping research direction, priorities, and standards.
- Design and guide coherent research projects and programmes that advance theLab’s long-term scientific capability and strategic intent.
- Engage with real-world problems as contexts for scientific inquiry, leading work to test, refine, and apply research ideas to achieve tangible impact.
- Exercise deep scientific judgement on complex or ambiguous problems, setting expectations for rigour, methodology, and interpretation.
- Act as a scientific authority in external engagements, partnerships, and collaborations, representing theLab’s work and contributing to its reputation.
- Contribute to theLab’s research profile through publication in top tier journals, open repositories, conference participation, and other externally visible outputs.
- Provide structured technical and intellectual mentorship to scientists across theLab, supporting the development of technical depth, judgement, and expertise.
- Shape and steward research-grade tools, libraries, and methodological frameworks as scientific artefacts, ensuring they support reproducibility, clarity, and reusability within research contexts.
- Influence theLab’s scientific culture by exemplifying high standards of rigour, curiosity, openness, and constructive challenge.
Duties may evolve, and you may be asked to take on other reasonable responsibilities within your competence to support our growth.
Ways of working
- Remain scientifically engaged through review, synthesis, and judgement, maintaining sufficient depth of understanding to credibly arbitrate priorities, standards, and trade-offs across theLab.
- Collaborate closely with the Director of AI to ensure research agendas are viable, well-supported, and appropriately resourced.
- Engage with Product and delivery teams through bounded scientific authority and consultation, without assuming ownership of execution.
Technical expertise and research standards:
We are looking for an exceptional scientist with the mathematical depth, research independence and engineering ability to develop genuinely new approaches to machine learning.
Essential
- Advanced qualification (PhD or equivalent research experience) in AI, machine learning, computational science, mathematics, physics, or related discipline.
- Exceptional mathematical foundations: Deep understanding of probability, statistics, linear algebra, optimisation and numerical methods, with the ability to derive, analyse and critically evaluate new ML algorithms from first principles.
- Independent scientific research: Demonstrable ability to identify important open problems, develop original hypotheses, design rigorous experiments and translate theoretical insights into novel algorithms.
- Ability to synthesise developments across multiple fields and identify long-term research opportunities.
- Experience contributing to major research collaborations and/or funding applications.
Technical capabilities
We don't expect any one person to bring all of the following. You'll have strong, evidenced experience in several of these areas, with real depth in at least one:
- Probabilistic machine learning: Strong theoretical and practical understanding of probabilistic modelling, approximate inference and uncertainty quantification. Able to reason rigorously about predictive uncertainty, calibration and model reliability.
- Deep learning and modern architectures: Detailed understanding of contemporary neural architectures, their mathematical foundations, training dynamics and limitations. Able to modify or develop architectures rather than simply fine-tune existing models.
- Experimental and empirical rigour: Experience developing meaningful benchmarks, constructing informative baselines, performing ablation studies and investigating failure modes. Able to distinguish genuine scientific advances from incremental performance improvements.
- Strong scientific software engineering: Advanced Python and PyTorch proficiency, with evidence of independently implementing complex algorithms, developing reliable research software and translating research prototypes into reusable capabilities.
- Research leadership and judgement: Ability to define and lead ambitious research programmes, challenge established approaches, mentor other scientists and make technically sound decisions under significant scientific uncertainty.
Particularly desirable
- Research experience in Bayesian deep learning, physics-informed machine learning, uncertainty quantification, probabilistic numerical methods or approximate inference.
- Experience developing new model architectures, including 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.
- A track record of original research contributions demonstrated through publications, open-source implementations or substantial technical research projects. Scientific originality and individual contribution matter 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.