Performance Engineer (Junior) | AI Infrastructure | Cambridge

Pure Resourcing Solutions; Dry Drayton; Hybrid; GBP 63,000 - 77,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Pure Resourcing Solutions is recruiting a junior Performance Engineer for our Cambridge AI infrastructure team. The role involves analyzing live training/inference metrics, building predictive models, and guiding cost-effective hardware decisions.

You will work closely with senior engineers in a hybrid Cambridge office, gaining hands-on experience with GPU workloads and profiling tools. The ideal candidate has a postgraduate background (PhD preferred or strong master's) in CS, maths, or physics,

Qualifications

• Postgraduate background required, PhD preferred, exceptional master's graduates considered
• Strong grasp of computer architecture, how LLMs and deep learning models run on hardware
• Experience building performance models or forecasting tools in Python or spreadsheets
• Hands-on work with GPU/accelerator code (CUDA) and profiling tools

Responsibilities

• Pull real metrics from live training and inference jobs and turn them into models and calculators
• Provide ownership early with senior support nearby
• Collaborate with senior engineers to answer questions on when an optimisation is worth shipping and if a different setup would be cheaper

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 abilityNice 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