Senior AI Infra Performance Engineer – Hybrid

Pure Resourcing Solutions; Cambridgeshire and Peterborough; Hybrid; GBP 90.000 - 120.000; Vollzeit

Benefits dieser Stelle

Hybrid work arrangement

Zusammenfassung

Pure Resourcing Solutions is sourcing a Senior Performance Engineer for a Cambridge-based AI infrastructure role with a hybrid working pattern. You will extract live metrics from training/inference runs to build models that forecast cost savings and performance gains.

The role requires strong skills in GPU/accelerator optimisation, LLM/DL hardware understanding, and proficiency with profiling and monitoring tools. A degree in CS/math is expected, with Python data tooling as a plus.

Qualifikationen

• A degree in computer science, mathematics, or something adjacent.
• A track record of building performance models or calculators (Python or spreadsheet-based) that forecast system behavior.
• Hands-on GPU/accelerator code optimisation using CUDA or similar.
• Solid understanding of how LLMs and DL run on real hardware, training vs inference.
• Proficient with profiling tools (Nsight, PyTorch Profiler) and monitoring (Prometheus, Grafana).
• Python for data work with Pandas and NumPy; comfortable with scripting.

Aufgaben

• Sit between research and engineering, pulling real numbers from live training and inference.
• Build models and calculators that translate data into actionable insights.
• Assess if optimisations justify spend and influence hardware decisions.
• Shape procurement and system design for the organisation and members.

Kenntnisse

Performance models

GPU/accelerator optimisation

Understanding of LLMs/DL on hardware

Profiling tools experience

Python for data work

Ausbildung

Degree in computer science, mathematics, or adjacent

Tools

Nsight

PyTorch Profiler

Prometheus

Grafana

Pandas

NumPy

Jobbeschreibung

Pure Resourcing Solutions is sourcing a Senior Performance Engineer for a Cambridge-based AI infrastructure role with a hybrid working pattern. You will extract live metrics from training/inference runs to build models that forecast cost savings and performance gains.

The role requires strong skills in GPU/accelerator optimisation, LLM/DL hardware understanding, and proficiency with profiling and monitoring tools. A degree in CS/math is expected, with Python data tooling as a plus.