ML Systems Engineer

ML Systems Engineer Overview

This opportunity is with an early-stage AI infrastructure company that is developing a persistent, high-speed knowledge layer for agentic AI. The business is a spin-out from a leading UK university and is currently hardware-led, with its software capability being built from the ground up. The role is centred on proving that the system works from the software side, with a strong emphasis on modelling, benchmarking, and technical validation.

The successful candidate will take ownership of the software-side evaluation work and work closely with the CTO. The focus is on creating evidence that demonstrates the platform’s performance and value before dedicated hardware is fully available, while also helping the company support pilots, fundraising, and broader technical credibility.

What you will be doing

• Lead the software modelling effort used to assess system behaviour in advance of dedicated hardware, effectively maintaining the digital twin used for evaluation.
• Develop agentic AI and GraphRAG workloads that can show measurable improvements at the system level.
• Build and maintain a benchmarking suite that tracks important metrics such as latency, GPU utilisation, token reduction, throughput, and cost per query.
• Design experiments that separate out the effect of the semantic memory layer on inference performance.
• Create enterprise knowledge graph datasets and define the evaluation approaches used to test them.
• Partner with hardware and systems teams so that the software model remains consistent with the real capabilities of the platform.
• Generate technical evidence that can be used in customer pilots, investor discussions, and internal validation work.
• Work directly with the CTO as the main owner of the software modelling and benchmarking stream.

What the company is looking for

• Commercial experience in AI systems, retrieval, or AI infrastructure, with a track record of shipping production software.
• Practical exposure to agentic pipelines, LLM fine-tuning, RAG or GraphRAG, and/or knowledge graph work.
• Strong Python ability, with confidence working across machine learning, distributed systems, and performance engineering topics.
• Demonstrated experience creating benchmark or evaluation frameworks with genuine methodological discipline.
• A systems-minded approach, strong personal initiative, and comfort working in ambiguous situations.
• The ability to communicate technical results clearly and convert them into evidence that others can understand and act on.
• Experience appropriate for someone who has already delivered software commercially, rather than someone coming purely from academia.
• Comfort working in a startup environment where the software function is still being established and processes are evolving.

Working arrangement and location

The role is described as hybrid, with Edinburgh as the primary location. Candidates in London, Cambridge, and other parts of the UK are also being considered.

Why this role may appeal

• It offers the chance to join a young company at an early stage and shape a core part of its technical direction.
• You will be working on a university spin-out with ambitious AI infrastructure goals.
• The role gives you direct exposure to the CTO and a high level of ownership over a critical technical workstream.
• Your work will have a direct impact on pilots, fundraising, and the company’s technical validation efforts.

How to apply

Interested candidates are invited to apply now. The posting also directs applicants to contact Charles Duran at IC Resources for further discussion.

The listing identifies Charles Duran as the relevant recruiter contact for this AI and machine learning opportunity.

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Degree Requirement: Not Specified

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