> Markdown version of [/jobs/ext/3882740-software-engineer-ai-systems-simulation](https://www.wearedevelopers.com/jobs/ext/3882740-software-engineer-ai-systems-simulation). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, AI Systems Simulation - **Company:** CommonAI C.I.C. - **Location:** Cambridge, UK - **Salary:** £60,000.0 - £105,000.0 - **Contract:** Permanent contract - **Skills:** C++ (Programming Language), Software Debugging, Discrete Event Simulation, Python (Programming Language), Software Tools, Simulation Software, Software Construction, Software Engineering, AI Infrastructure, Rust (Programming Language), Large Language Models, AI Coding Agents, Performance Monitor, SGLang - **Published:** October 23, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5938018306 ## About the Role Strong software engineering skills and evidence of building, debugging and maintaining substantial technical software. - Experience in one or more of: discrete-event simulation; computer architecture or performance modelling; distributed/HPC systems; inference runtimes and serving; memory or network modelling; integration of scientific software and heterogeneous components. - The ability to understand unfamiliar code, reason about interfaces and assess the suitability and maintenance costs of reusing, extending or building software. - A collaborative approach to testing assumptions and working with specialists in adjacent areas. - Practical experience using AI coding agents, with the judgement to check their output for correctness and maintainability. Python, C++, Rust or other relevant systems or scientific programming experience can provide a route into the role. Familiarity with vLLM or SGLang, simulator frameworks, or hardware performance tools would be useful. Depth in a subset of these areas is sufficient; languages and frameworks will follow the engineering needs of the programme., 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. ## Description 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 Build simulation software that helps engineers and investors explore how AI systems would behave with different processors, memory systems, networks or serving strategies. Your work will help turn models and existing software into a practical framework for comparing design choices against realistic LLM workloads. You will develop and connect simulation components, making sure that changes in one part of a system have the appropriate consequences elsewhere. This means understanding unfamiliar code, designing clear interfaces and building software whose predictions can be investigated and tested against measurements. We aim to reuse and extend existing frameworks wherever they provide a sound foundation. We welcome depth in one or more relevant areas, including simulation, systems software, performance modelling and inference serving. Your initial focus will reflect your strengths and the components we choose to build on, with complementary expertise added as the team grows. What you will do - Develop and integrate simulation components that capture behaviour and interactions relevant to an AI infrastructure comparison. - Work with the technical lead and our partners to evaluate existing frameworks and software, extending and connecting useful components through clear interfaces. - Develop appropriate models of system state and resource use so that changes in one component have the appropriate consequences elsewhere. - Build reproducible simulation scenarios, diagnostic outputs and tests that expose incorrect assumptions, integration errors and unsupported cases. - Work with measurement and uncertainty colleagues to compare predictions with evidence, improve models and integrate calibration and uncertainty methods into the simulator. - Maintain reliable software, documentation and development workflows, using AI coding agents to support implementation and testing. An example early project would be to bridge an existing simulator component with our workload models and measurement datasets, producing a reproducible comparison between predicted and measured performance over two systems.