ML Systems Engineer

IC Resources; City of Edinburgh; Hybrid; GBP 65,000 - 100,000; Full time

Job summary

IC Resources is recruiting a Machine Learning Engineer for an Edinburgh-based AI startup. The role blends software modelling with benchmarking to validate system behaviour in a hybrid, UK-wide context.

You will develop agentic AI workloads, build a robust benchmark suite, and design experiments to measure how the semantic memory layer affects inference performance. Strong Python and distributed-systems know-how are essential.

Qualifications

• Commercial experience in AI systems, retrieval, or AI infrastructure with shipped production software.
• Hands-on with agentic pipelines, LLM fine-tuning, RAG/GraphRAG, or knowledge graphs.
• Strong Python and comfort across ML, distributed systems, and performance engineering.
• Track record building benchmarks/eval frameworks with rigour.

Responsibilities

• Own the software model used to evaluate system behaviour ahead of hardware.
• Build agentic AI and GraphRAG workloads showing measurable improvements.
• Develop and maintain a benchmark suite (latency, GPU utilisation, token reduction).
• Design experiments to isolate the semantic memory layer's impact on inference.
• Create enterprise knowledge graph datasets and evaluation methodologies.
• Collaborate with hardware/systems teams to keep software aligned with capability.
• Generate evidence to support pilots, fundraising, and technical validation.

Skills

Python

LLM fine-tuning

RAG/GraphRAG

Knowledge graphs

Performance engineering

Distributed systems

Benchmark frameworks

Communication

Tools

PyTorch

TensorFlow

Graph databases

GPU profiling tools

Job description

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