Senior ML Compute Architect: Scalable GPU Pipelines

Ellison Institute of Technology; Oxford; On-site; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Travel allowance

Pension 7.5%

Private Medical Insurance

Life Assurance

Cycle to Work

Job summary

Ellison Institute of Technology (EIT) invites applications for a role leading the design, build, and operation of high‑performance ML compute clusters at scale in Oxford. The role focuses on reliable, secure platforms, automated guardrails, and scalable experimentation pipelines.

You will collaborate with Research, Data, and Applied teams to forecast capacity and drive cost efficiency. You must be onsite in Oxford with a willingness to travel; sponsorship possibilities may be assessed

Qualifications

• Proven experience leading the design, build, and operation of high‑performance ML compute clusters at scale.
• Autonomous systems design with ability to ideate and implement optimal solutions.
• Exposure to migrating ML infrastructure to containerised systems.
• Expertise with high‑throughput storage systems for ML/HPC workloads.
• Expert-level understanding of GPU architecture and high‑speed networking for distributed training.

Responsibilities

• Build, operate, and optimise high‑performance GPU training/inference clusters for scalable ML workloads.
• Design and implement high‑throughput data paths, optimise I/O and data locality across compute/storage.
• Benchmark, profile, and resolve performance bottlenecks across compute, network and orchestration layers.
• Establish observability, resilience, and automated security controls for sensitive research environments.
• Forecast capacity and cost for GPU/storage needs with cross‑team collaboration.

Skills

ML compute clusters

GPU architectures

High-throughput storage

Iac and CI/CD

Terraform

Argo CD

Tools

Terraform

Argo CD

Job description

Ellison Institute of Technology (EIT) invites applications for a role leading the design, build, and operation of high‑performance ML compute clusters at scale in Oxford. The role focuses on reliable, secure platforms, automated guardrails, and scalable experimentation pipelines.

You will collaborate with Research, Data, and Applied teams to forecast capacity and drive cost efficiency. You must be onsite in Oxford with a willingness to travel; sponsorship possibilities may be assessed