Founding AI Training Infrastructure Architect

Imperial College London

Greater London

On-site

GBP 90,000 - 130,000

Full time

8 days ago
Application generator

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Benefits offered by this job

Equity
Founding-team influence
Small team culture

Job summary

Imperial College London in the United Kingdom invites applications from an exceptionally skilled engineer to build and own our AI training infrastructure. You will work across research and production environments to design scalable systems that run on hundreds of GPUs across cloud providers, emphasizing performance, reliability, and maintainability.

You will collaborate with researchers and platform engineers to optimize throughput, memory management and networking while advancing MLOps tooling

Qualifications

  • Experience with large-scale distributed training and PyTorch.
  • Strong background in HPC, GPU clusters and memory management.
  • Proficient Python and production-grade software engineering.
  • Experience with MLOps tooling and CI/CD.
  • Ability to work across research and production environments.
  • Familiarity with cloud platforms (AWS) and GPU cloud providers.
  • Experience with computer vision or multimodal AI is a plus.

Responsibilities

  • Design and own large-scale AI training infrastructure.
  • Scale distributed model training across hundreds of GPUs and multiple clouds.
  • Optimize training throughput and GPU utilization.

Skills

PyTorch
Distributed training
Kubernetes
Slurm
AWS
HPC
MLOps tooling
Python
GPU orchestration

Tools

Kubernetes
Slurm
GPU orchestration
AWS

Job description

Imperial College London in the United Kingdom invites applications from an exceptionally skilled engineer to build and own our AI training infrastructure. You will work across research and production environments to design scalable systems that run on hundreds of GPUs across cloud providers, emphasizing performance, reliability, and maintainability.

You will collaborate with researchers and platform engineers to optimize throughput, memory management and networking while advancing MLOps tooling

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