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

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

Benefits offered by this job

Pension

Hybrid from Cambridge office

Job summary

Cambridge AI Infrastructure is seeking a junior Performance Engineer to join the AI infrastructure team. You will work with senior engineers to extract metrics from live training/inference jobs and translate them into predictive models and cost calculators to inform shipping decisions.

Applicants should have a postgraduate research background (PhD preferred) with strong knowledge of computer architecture, GPU programming (CUDA) and profiling/monitoring tools.

Qualifications

• Postgraduate research background, ideally a PhD, with exceptional academic record.
• Strong grasp of computer architecture and how LLMs run on hardware, training vs inference, matrix multiplication, KV-caching.
• Experience building performance models or forecasting tools in Python or spreadsheets.
• Hands-on work with GPU/accelerator code (CUDA or similar).
• Familiarity with profiling tools and monitoring stacks such as Prometheus, Grafana.
• Strong Python for data work; Pandas and NumPy; scripting ability.

Responsibilities

• Work with senior engineers to pull real metrics from live training and inference jobs and turn them into models and calculators to decide if an optimisation is worth shipping.
• Own model development early with close senior support.
• Collaborate across AI infrastructure problems to improve cost and performance decisions.

Skills

Postgraduate research background

Computer architecture fundamentals

Performance modelling / forecasting

Python for data work

GPU / accelerator code (CUDA)

Profiling tools (Nsight, PyTorch Prof)

Monitoring stacks (Prometheus, Grafana

Education

PhD in CS / Math / Physics or related field

Master's degree (relevant coursework)

Tools

CUDA

Nsight

Prometheus

Grafana

vLLM

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 NumX g, 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
• exposure to people across the wider AI and academic scene most people this early in their career don't get