Performance Engineer (Junior) | AI Infrastructure | Cambridge |

Pure Resourcing Solutions; Cambridge; Hybride; GBP 42 000 - 70 000; Plein temps

Avantages offerts par ce poste

Pension

Hybrid work Cambridge office

Résumé du poste

Pure Resourcing Solutions is recruiting a junior Performance Engineer in Cambridge. You will work with senior engineers to capture live training/inference metrics and build predictive models to guide deployment decisions.

The role targets exceptional early-career professionals with a strong academic background and hands-on GPU experience, offering a hybrid Cambridge office setup and a path into AI infrastructure.

Qualifications

• Postgraduate research background, ideally a PhD in CS, maths, physics, or a related field.
• Strong grasp of computer architecture fundamentals and how LLMs run on hardware.
• Experience building performance models or forecasting tools in Python or spreadsheets.
• Hands-on work with GPU/accelerator code (CUDA).
• Familiarity with profiling tools (Nsight, PyTorch Profiler) and monitoring stacks (Prometheus, Grafana).
• Strong Python for data work with Pandas and NumPy.

Responsabilités

• Pull real metrics from live training and inference jobs and turn them into models and calculators.
• Assess whether an optimisation is worth shipping and whether a different setup would run cheaper.
• Take real ownership early with senior support close by.
• Collaborate with senior engineers to improve AI infrastructure.

Formation

PhD in computer science or related field

Outils

Nsight

PyTorch Profiler

Prometheus

Grafana

CUDA

Description du poste

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, 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.