ML Systems Engineer

Intellectual Capital Resources; City of Edinburgh; Hybrid; GBP 59,000 - 99,000; Full time

Job summary

Intellectual Capital Resources is seeking an experienced AI infrastructure leader for an Edinburgh-based hybrid role, with UK-wide candidates considered. You will own the digital twin software model used to evaluate system behavior before hardware is deployed and will drive agentic AI workloads with measurable system-level improvements.

The role requires strong Python skills, expertise in LLM fine-tuning and RAG, and a track record of designing robust benchmarks.

Qualifications

• Commercial experience in AI systems, with production software shipped.
• Hands-on experience with agentic pipelines, LLM fine-tuning, RAG/GraphRAG, or knowledge graphs.
• Strong Python skills, across ML, distributed systems, and performance engineering.
• Track record of building benchmarks and evaluation frameworks with rigour.
• Systems thinker with high agency and comfort with ambiguity.
• Strong 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 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.

Skills

AI systems

Agentic pipelines

Python

Benchmarks

Systems thinking

Communication

Job description

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

Responsibilities:

• 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

Technologies:

• 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 37 week of 2026