Junior Performance Engineer - AI Infrastructure (Hybrid Cambridge)

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

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.