Senior Performance Engineer | AI Infrastructure | Cambridge (Hybrid)

Pure Resourcing Solutions Limited; Dry Drayton; Hybrid; GBP 90,000 - 120,000; Full time

Benefits offered by this job

Competitive salary

Pension

Hybrid from Cambridge office

Exposure to AI/academic community

Job summary

Pure Resourcing Solutions Limited in Cambridge invites applications for Senior Performance Engineer, focusing on AI infrastructure. The role sits between research and engineering, turning live training and inference data into actionable models that evaluate optimizer ROI and hardware choices.

The successful candidate will design performance calculators, work with Python, CUDA, and DL tooling, and help shape purchasing and architecture across an ambitious AI ecosystem.

Qualifications

• A degree in computer science, mathematics, or something adjacent.
• A track record of building performance models or calculators (Python or spreadsheet-based) that forecast how a system will behave.
• Hands-on GPU/accelerator code optimisation, CUDA or similar.
• Genuine understanding of how LLMs and deep learning models run on hardware.
• Comfortable using Nsight or PyTorch Profiler and monitoring stacks.
• Python for data work with Pandas and NumPy.

Responsibilities

• Sit between research and engineering to extract live training/inference metrics.
• Build models and calculators to answer cost/benefit of optimisations.
• Assess ROI of accelerators and architecture changes.
• Influence purchasing and system design across the org and members.

Skills

Degree in CS/Math

Performance models/calculators

GPU/accelerator optimization

LLMs and DL hardware understanding

Profiling (Nsight)

Monitoring stacks (Prometheus Grafana)

Python data work (Pandas NumPy)

Postgraduate/research background

vLLM / inference serving

Open source contributions

Education

Degree in computer science or mathematics

Tools

Nsight

PyTorch Profiler

Prometheus

Grafana

Python

Pandas

NumPy

vLLM

CUDA

Job description

Senior Performance Engineer | AI Infrastructure | Cambridge (Hybrid) | £90k-£120k

Nobody quite knows where their compute budget is actually going until someone builds the model that tells them. That's this role. My client is a Cambridge-based non-profit that exists to stop different parts of the AI world quietly rebuilding the same infrastructure. Rather than a startup, a big enterprise, a government department and a university lab each working out GPU efficiency from scratch, they pool the hard problems and the expertise needed to solve them, so everyone moves faster. It's early days for the organisation but there's serious momentum and serious financial backing behind it already. They're hiring Performance Engineers at junior and senior level, to sit at the sharp end of that mission.

Day to day

You'd sit between the research and engineering teams, pulling real numbers off live training and inference runs rather than working from theory. From there, the job is building the models and calculators that turn those numbers into an actual answer: will this optimisation help, would a different accelerator be worth the spend, is this architecture change going to pay for itself. Those answers don't stay internal either, they shape what gets bought and how systems get built, for the organisation itself and for everyone else in the membership relying on that judgement.

What you'll bring

• A degree in computer science, mathematics, or something adjacent
• A track record of building performance models or calculators (Python or spreadsheet-based) that actually forecast how a system will behave
• Hands-on GPU/accelerator code optimisation, CUDA or similar
• Genuine understanding of how LLMs and deep learning models run on real hardware, training versus inference, matrix multiplication, KV-caching, that level of detail
• Comfortable in profiling tools like Nsight or PyTorch Profiler, and monitoring stacks like Prometheus and Grafana
• Python for data work, Pandas and NumPy, plus general scriptingNice to have rather than essential: a postgraduate degree and research background (publications welcome), real depth on inference serving frameworks like vLLM, a stats background, and any open source or research contributions.

Why look twice at this one

It's a rare early seat at something with genuine backing and genuine ambition, where the work you do gets acted on rather than filed away.

• Competitive salary and pension, hybrid from a Cambridge office, and real exposure to people across the wider AI and academic scene