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