Graduate Cloud Platform Engineer — AI Infra & Flexible Work

Graphcore; West of England; On-site; GBP 32,000 - 42,000; Full time

Benefits offered by this job

Flexible working

Pension up to 5%

Phantom equity

On-site barista and free food

Income protection and life assurance

EV salary sacrifice

Private medical insurance

Dental cover

Cycle to work

Job summary

Graphcore in Bristol offers a two-year Graduate Cloud Platform Engineer programme where you’ll develop, deploy and operate services across Graphcore’s high‑performance AI infrastructure. You’ll gain hands‑on experience with Linux, cloud technologies, networking and CI/CD while collaborating with engineers and researchers.

You’ll contribute to automation, logs and metrics analysis and learn how OpenStack, Kubernetes, and infrastructure as code fit into scalable AI platforms.

Qualifications

• Bachelor's degree in CS/SE/IT or related, completed by summer 2027 or within two years.
• Experience with the Linux command line, and scripting/programming (Python or Bash).
• Familiarity with Git or another version-control system, with documentation practices.
• Methodical troubleshooting and ability to communicate findings with teammates.

Responsibilities

• Develop, deploy and operate services across Graphcore’s cloud infrastructure.
• Write automation, investigate issues, analyse logs and metrics.
• Support platforms used by internal teams and Graphcore cloud customers.
• Collaborate with engineers, researchers and Datacentre Operations.

Skills

Linux CLI

Python or Bash

Git or VCS

Troubleshooting

Education

Bachelor's degree in Computer Science or related

Job description

Graphcore in Bristol offers a two-year Graduate Cloud Platform Engineer programme where you’ll develop, deploy and operate services across Graphcore’s high‑performance AI infrastructure. You’ll gain hands‑on experience with Linux, cloud technologies, networking and CI/CD while collaborating with engineers and researchers.

You’ll contribute to automation, logs and metrics analysis and learn how OpenStack, Kubernetes, and infrastructure as code fit into scalable AI platforms.