Senior Performance Engineer

Employment: Full-time Experience: Mid-Senior level Education: Bachelor's Degree CommonAI CIC is a non-profit membership organisation, founded on a belief in collaborative engineering for the safe and responsible development of foundational AI technologies. A place where AI startups, enterprises large and small, public sector bodies and academia can share resources and knowledge, to codevelop and grow businesses, fast. We are seeking a Senior Performance Engineer to join our rapidly growing team. In this role, you will work with AI researchers and software engineers to build up a detailed understanding of how their applications are performing. You will instrument and collect granular metrics from inference and training jobs and use that information to develop sophisticated mathematical models that predict how software optimisations and architectural or hardware changes will impact system performance. Your work will directly influence both our in-house and member’s hardware purchasing decisions and architectural optimisations, ensuring teams can run AI workloads efficiently and cost-effectively. Requirements This role requires a degree in computer science, mathematics or an adjacent field. You should also be able to demonstrate: Significant experience building insightful mathematical models and performance calculators (Excel/Google Sheets or Python modelling experience) to forecast system behaviour. Optimisation of code running on GPUs and/or other accelerators (e.g. CUDA). Solid understanding of computer architecture fundamentals and how LLMs and Deep Learning models execute on that hardware (inference vs. training, matrix multiplication, KV-caching, etc.). Proficiency with profiling tools (NVIDIA Nsight, PyTorch Profiler) and monitoring stacks (Prometheus, Grafana). Capability to work in Python for data analysis (Pandas, NumPy) and scripting. The following are also highly valued: Post-graduate degrees and research experience in relevant fields (please list your publications). Deep understanding of inference serving frameworks (e.g. vLLM). Background in statistical analysis. Contributions to open source and/or research projects. Benefits A collaborative and supportive work environment The opportunity to have a high impact in a growing organisation Competitive salary package and pension Professional development opportunities Networking opportunities with influential people from across the tech sector and academia A vibrant office environment located a few minutes’ walk away from Cambridge train station