Simulation Validation and Uncertainty Engineer
CommonAI C.I.C.
Simulation Validation and Uncertainty Engineer
CommonAI C.I.C.
Non-profit AI collaboration platform and compute
CommonAI C.I.C. · Cambridge, United Kingdom (Hybrid) · Full-time · Posted 2026-10-22
Salary: GBP 60,000–105,000
Workplace: hybrid
Department: Scaling Inference
About CommonAI
CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems.
Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing.
The Opportunity
Make uncertainty a first-class output of an AI systems simulator. When a simulation predicts that one design will outperform another, you will help establish how much confidence the evidence supports, which assumptions could change the conclusion, and which experiments would reduce the uncertainty that matters to the decision.
You will combine mathematical reasoning with practical software engineering to calibrate models against measurements, test predictions on independent cases and build uncertainty analysis into the simulation workflow. Your methods and software will help engineers and investors understand what the simulator predicts and the limits of those predictions.
We welcome people from AI systems, HPC and other fields where simulation is tested against experiments. You might bring systems engineering experience and an interest in developing uncertainty methods, or experience in mathematical modelling and scientific computing that you want to apply to AI infrastructure.
What you will do
- Develop and implement methods to calibrate simulator models against measurements and evaluate predictions on cases that were not used for calibration.
- Distinguish uncertainty arising from measurements, model assumptions and parameters, operational variation and limited simulation runs, and represent it appropriately.
- Analyse sensitivity and uncertainty in comparisons between alternatives, accounting for shared sources of uncertainty and their effects on the comparison.
- Develop outputs that show how uncertain a prediction is, when a ranking could change, and when a result extends beyond the evidence used to validate it.
- Work with measurement and workload engineers to design experiments that test modelling assumptions and reduce uncertainty that matters to the decision.
- Integrate these methods into repeatable simulation workflows and communicate their assumptions and limitations to engineers and the technical analyst.
An example early project might be to establish a calibration and validation workflow for the a comparison between two architectures, with uncertainty outputs that help explain the result. You will develop and review the methodology with the technical lead and Scientific Director, SIL.
- Mathematical maturity: the ability to reason carefully about models, data, variation and the assumptions behind a quantitative claim.
- Software engineering experience sufficient to turn numerical methods or analysis into readable, tested, reusable tools.
- Relevant experience in one or more of: quantitative systems performance analysis, statistical modelling, simulation validation, uncertainty quantification, scientific/HPC software or the theory of experimental design.
- The ability to collaborate with specialists and explain both a result and the limits of the evidence supporting it.
- Practical experience using AI coding agents and checking their output.
Experience with model discrepancy, sensitivity analysis, Bayesian methods, surrogate models or sequential experimental design would be useful. So would familiarity with computer architecture or inference systems. We welcome complementary backgrounds: strong systems engineers developing uncertainty expertise, and engineers with modelling or uncertainty expertise moving into AI infrastructure.
Why join us
- Help build UK AI capability and improve how investment and compute serve the public good.
- Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities.
- Shape a growing team with startup pace and close academic links.
- Use frontier models and coding agents with a substantial tooling budget.
Salary range: £60,000 - £110,000 per annum
Working with us
Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person.
We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work.
CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace.
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Non-profit AI collaboration platform and compute
CommonAI C.I.C. provides a collaborative platform and shared AI infrastructure in the UK through three connected entities: a non-profit member organization guiding the ecosystem, CommonAI Holdings connecting investors with developers, and CommonAI Compute offering affordable GPU resources. Members such as startups, SMEs, public bodies, and universities can co-develop and access a Digital Commons with models, training data, and hardware designs. The platform lowers barriers to AI development by providing computing power, strategic expertise, and access to investors and experts within a membership model. Its goal is to level the playing field for AI players by enabling open collaboration, shared IP, and connected capital to compete globally.
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