ML Systems Engineer
Summary
ML Systems Engineer based in Theale, Reading (UK) building production AI infrastructure — agentic pipelines, LLM fine-tuning, RAG/GraphRAG and knowledge graphs. Day-to-day work centers on strong Python, distributed systems, performance engineering, and building rigorous benchmarks and evaluation frameworks. Salary £59,000–£99,000 per year.
Salary: £59,000 - 99,000 per year
Requirements:- Commercial experience in AI systems, retrieval, or AI infrastructure, with production software shipped
- Hands-on experience with agentic pipelines, LLM fine-tuning, RAG/GraphRAG, or knowledge graphs
- Strong Python skills, with comfort across ML, distributed systems, and performance engineering
- Track record of building benchmarks and evaluation frameworks with real rigour
- Systems thinker with high agency and comfort with ambiguity
- Strong communication skills, able to translate technical results into clear evidence
- Own the software model (digital twin) used to evaluate system behaviour ahead of dedicated hardware
- Build agentic AI and GraphRAG workloads that show measurable system-level improvements
- Build and maintain a benchmark suite covering latency, GPU utilisation, token reduction, throughput, and cost per query
- Design experiments that isolate the impact of the semantic memory layer on inference performance
- Develop enterprise knowledge graph datasets and evaluation methodologies
- Work with hardware and systems teams to keep software models aligned with hardware capability
- Generate evidence to support pilots, fundraising, and technical validation
- Agentic AI
- AI
- AI Agents
- CTO
- Fine-tuning
- Hardware
- Support
- LLM
- Python
- RAG
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 36 week of 2026