ML Systems Engineer

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

• 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