Simulation Validation Engineer

CommonAI CIC

. Develop and implement methods to calibrate simulator models against measurements .

Posted 10/23/2026full-timeCambridge • United KingdomMid-LevelSenior💰 £60,000 - £105,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and implementing calibration methods for simulator models, with a strong focus on uncertainty quantification and statistical modeling. Capable of collaborating with engineers to design experiments and communicate complex results effectively.

Highest-signal resume keywords

Uncertainty QuantificationStatistical ModellingSimulation ValidationSensitivity AnalysisSoftware Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Calibration MethodsQuantitative Systems Performance AnalysisBayesian MethodsSurrogate ModelsExperimental Design Theory

Soft Skills

CollaborationCommunication

Tools & Technologies

AI Coding AgentsHPC Software

Industry Keywords

Model DiscrepancyInference SystemsComputer Architecture

About the role

Key responsibilities & impact

Develop and implement methods to calibrate simulator models against measurements
Evaluate predictions on cases not used for calibration
Distinguish uncertainty from measurements, model assumptions and parameters, operational variation and limited simulation runs
Represent uncertainty appropriately in simulation outputs
Analyse sensitivity and uncertainty when comparing alternatives, including shared uncertainty sources
Develop outputs showing prediction uncertainty, when rankings could change and when results extend beyond validation evidence
Work with measurement and workload engineers to design experiments testing modelling assumptions and reducing decision-relevant uncertainty
Integrate methods into repeatable simulation workflows
Communicate assumptions and limitations to engineers and the technical analyst
Establish calibration and validation workflows for architecture comparisons
Develop and review methodology with the technical lead and Scientific Director, SIL

Requirements

What you’ll need

Ability to reason carefully about models, data, variation and assumptions behind quantitative claims
Software engineering experience sufficient to turn numerical methods or analysis into readable, tested, reusable tools
Relevant experience in quantitative systems performance analysis, statistical modelling, simulation validation, uncertainty quantification, scientific/HPC software or experimental design theory
Ability to collaborate with specialists and explain results and limits of supporting evidence
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
Familiarity with computer architecture or inference systems would be useful
Earlier-career candidates considered with strong evidence of ability
Relevant postgraduate study is useful but not required; qualifications are secondary to experience and quality of work

Benefits

Comp & perks

A collaborative and supportive work environment
The opportunity to have a high impact in a growing organisation
Competitive salary package, including pension and benefits
Professional development opportunities
Networking opportunities with influential figures from across the tech sector and academia
A vibrant office environment located a few minutes’ walk away from Cambridge train station
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
Hybrid working with Cambridge attendance agreed individually
Permanent employment, part-time arrangements and consultancy options
Flexible start date