Performance Engineer (Junior) | AI Infrastructure | Cambridge (Hybrid)

Pure Resourcing Solutions Limited; Dry Drayton; Hybrid; GBP 42,000 - 70,000; Full time

Benefits offered by this job

Pension

Hybrid from Cambridge office

Job summary

Pure Resourcing Solutions Limited is seeking a Junior Performance Engineer for the Cambridge AI infrastructure team. You will help build models and calculators from live training/inference metrics to predict performance and cost before hardware changes are shipped.

You'll work with senior engineers, gaining hands-on experience in GPU/accelerator workflows, Python-based tooling, and profiling. The role blends research rigor with practical impact in a hybrid Cambridge office.

Qualifications

• Postgraduate research background; PhD preferred.
• Strong grasp of computer architecture and how DL runs on hardware.
• Experience building performance models or forecasting tools in Python or spreadsheets.
• Hands-on CUDA or similar accelerator code experience.
• Profiling with Nsight or PyTorch Profiler; exposure to Prometheus & Grafana.
• Strong Python for data work; Pandas and NumPy.

Responsibilities

• Work with senior engineers to turn live metrics into performance models and calculators.
• Assess whether an optimization is worth shipping and whether a setup would run cheaper.
• Own parts of performance modeling early with close senior support.
• Collaborate with AI infrastructure stakeholders on ongoing improvements.

Skills

Computer architecture fundamentals

Python for data work

Pandas & NumPy

CUDA or similar

Profiling tools (Nsight, PyTorch Prof"

Scripting/automation

Inference/LLMs knowledge

Education

PhD in CS/Math/Physics

Masters acceptable

Tools

Nsight

Prometheus

Grafana

Job description

Performance Engineer (Junior) | AI Infrastructure | Cambridge (Hybrid) | up to £70k

Most engineers find out whether a change works after it ships. This role is about knowing before anyone spends a penny on new hardware, building the models that predict itMy client is a Cambridge-based non-profit built on a fairly simple premise: different bits of the AI world keep solving the same infrastructure problems separately, and that's wasteful. So they've built a shared space where startups, big enterprises, government bodies and university researchers can pool that hard technical work instead. Early days as an organisation, but real backing and real momentum behind it.

This particular seat is for an early career professional. We're after someone academically exceptional, ideally with a PhD, who's ready to get stuck into real technical work quickly rather than needing a long runway to get there.

Day to day

You'd work alongside senior engineers on the team, pulling real metrics off live training and inference jobs and turning them into models and calculators that answer actual questions, whether an optimisation is worth shipping, whether a different setup would run cheaper. Real ownership early, with senior support close by.

What you'll bring

• A postgraduate research background, ideally a PhD in computer science, mathematics, physics or a closely related field, strongly preferred, exceptional recent master's graduates with directly relevant coursework will also be considered
• A genuine, demonstrated grasp of computer architecture fundamentals and how LLMs and deep learning models actually run on hardware, training versus inference, matrix multiplication, KV-caching
• Real experience building performance models or forecasting tools, Python or spreadsheet-based, from research, a thesis, a placement, or serious personal projects
• Hands-on work with GPU or accelerator code, CUDA or similar
• Familiarity with profiling tools (Nsight, PyTorch Profiler) and ideally some exposure to monitoring stacks (Prometheus, Grafana)
• Strong Python for data work, Pandas and NumPy, genuine scripting ability
• Nice to have: exposure to inference serving frameworks like vLLM, published research, or open source contributions in this space.

Why look twice at this one

An early route into industry for someone whose academic record speaks for itself, working directly with senior engineers on problems with real backing behind them. Pension, hybrid from a Cambridge office, and exposure to people across the wider AI and academic scene most people this early in their career don't get