About this role
Salary: £59,000 - 99,000 per year
Requirements: Commercial experience in AI systems, retrieval, or AI infrastructure, with production software shippedHands-on experience with agentic pipelines, LLM fine-tuning, RAG/GraphRAG, or knowledge graphsStrong Python skills, with comfort across ML, distributed systems, and performance engineeringTrack record of building benchmarks and evaluation frameworks with real rigourSystems thinker with high agency and comfort with ambiguityStrong communication skills, able to translate technical results into clear evidence Responsibilities: Own the software model (digital twin) used to evaluate system behaviour ahead of dedicated hardwareBuild agentic AI and GraphRAG workloads that show measurable system-level improvementsBuild and maintain a benchmark suite covering latency, GPU utilisation, token reduction, throughput, and cost per queryDesign experiments that isolate the impact of the semantic memory layer on inference performanceDevelop enterprise knowledge graph datasets and evaluation methodologiesWork with hardware and systems teams to keep software models aligned with hardware capabilityGenerate evidence to support pilots, fundraising, and technical validation Technologies: Agentic AIAIAI AgentsCTOFine-tuningHardwareSupportLLMPythonRAG More:
We are an early-stage AI infrastructure company building a persistent, high-speed knowledge layer for agentic AI, enabling thousands of AI agents to query a shared knowledge base concurrently. We are spinning out of a leading UK university and are currently hardware-led while building out our software capability from scratch. This is an Edinburgh-based hybrid role, with UK-wide candidates also considered, and you will work closely with our CTO on the software-side modelling and benchmarking that proves the system works.
last updated 40 week of 2026