ML Systems Engineer

Machine Learning Engineer, AI Startup (Edinburgh, hybrid, UK-wide considered) An early-stage AI infrastructure company building a persistent, high-speed knowledge layer for agentic AI, letting thousands of AI agents query a shared knowledge base concurrently. Spinning out of a leading UK university, currently hardware-led and building out its software capability from scratch. The role : Own the software-side modelling and benchmarking that proves the system works, working closely with the CTO. What You’ll Do * Own the software model (“digital twin”) used to evaluate system behaviour ahead of dedicated hardware * Build agentic AI and GraphRAG workloads showing measurable system-level improvements * Build and maintain a benchmark suite (latency, GPU utilisation, token reduction, throughput, cost per query) * Design experiments isolating the impact of the semantic memory layer on inference performance * Develop enterprise knowledge graph datasets and evaluation methodologies * Work with hardware/systems teams to keep software models aligned with hardware capability * Generate evidence to support pilots, fundraising, and technical validation What We’re Looking For * Commercial experience in AI systems, retrieval, or AI infrastructure, having shipped production software * Hands-on experience with agentic pipelines, LLM fine-tuning, RAG/GraphRAG, or knowledge graphs * Strong Python, comfortable across ML, distributed systems, and performance engineering * Track record building benchmarks/eval frameworks with real rigour * Systems thinker, high agency, comfortable with ambiguity * Strong communicator able to translate technical results into clear evidence Please contact Charles Duran at IC Resources.