Engineering and infrastructure

AI data centre networks and facilities

AI data centre networks and facilities covers the people who supply compute sites with power, cooling and network capacity. AI companies need them because accelerators are only useful when they are powered, kept cool and connected to each other.

Model builders now plan compute in terms of sites and power, not just chips. Racks draw far more electricity and give off far more heat than older server halls, and training jobs need very fast links between thousands of accelerators. These hires are tricky because the talent sits in utilities, telecoms and traditional data centres. Few people have worked at this density, so you often hire for adjacent experience and teach the rest.

Hiring now 6

Roles in this family

6 roles

Hiring nowAI Cluster Network EngineerAn AI Cluster Network Engineer designs and runs the network that links accelerators during distributed training. A well-built fabric keeps expensive…Hiring nowAI Data Centre Power EngineerAn AI Data Centre Power Engineer plans the electrical capacity and resilience of AI compute sites. They make sure GPU clusters get safe, reliable…Hiring nowAI Thermal Systems EngineerAn AI Thermal Systems Engineer designs and validates the cooling for dense accelerator racks. Good cooling keeps expensive compute inside…Hiring nowAI Compute Scheduler EngineerAn AI Compute Scheduler Engineer decides which model workloads get which cluster resources, and when. Good scheduling keeps expensive GPUs busy…Hiring nowAI Data Centre Programme ManagerAn AI Data Centre Programme Manager delivers gigawatt-scale compute sites, covering land, power, cooling and schedule. They turn a company's compute…Hiring nowAI Power and Energy StrategistThe AI Power and Energy Strategist secures the electricity that AI compute sites run on. Without firm power on a workable schedule, GPUs sit idle…

Where the people come from

Network, electrical, mechanical, facilities or HPC engineering

Not to be confused with. These are enabling infrastructure professions rather than model-development roles.

Core skills

  • Distributed systems
  • Cloud and orchestration
  • Hardware and computer architecture
  • Reliability and incident response

Where it is used

  • Semiconductors and compute infrastructure
  • Energy and utilities

More in Engineering and infrastructure

Applied AI and machine learning engineering0 rolesDistributed training and research systems0 rolesInference and model optimisation0 rolesMLOps and AI platform engineering0 rolesAI observability, reliability and FinOps0 rolesAI compilers, kernels and runtime systems0 rolesAI accelerators and hardware co-design0 rolesPrivacy-preserving and confidential AI0 roles

← All new job families

Start a search

Hiring in aI data centre networks and facilities?

Tell us the work you need done. We find the people already doing it, whatever their job title says.