Senior Platform Engineer

Ellison Institute of Technology
Oxford, United Kingdom
3 weeks ago
Work Location
Hybrid
Seniority
Senior
Posted
8 Sep 2026 (3 weeks ago)

Join is at EIT:

At the Ellison Institute of Technology (EIT), we’re on a mission to translate scientific discovery into real world impact. We bring together visionary scientists, technologists, policy makers, and entrepreneurs to tackle humanity’s greatest challenges in four transformative areas:

  • Health, Medical Science & Generative Biology
  • Food Security & Sustainable Agriculture
  • Climate Change & Managing CO₂
  • Artificial Intelligence & Robotics

This is ambitious work - work that demands curiosity, courage, and a relentless drive to make a difference. At EIT, you’ll join a community built on excellence, innovation, tenacity, trust, and collaboration, where bold ideas become real-world breakthroughs. Together, we push boundaries, embrace complexity, and create solutions to scale ideas for lab to society. Explore more at www.eit.org

Your Role:

Join our SciComp team to build the cloud and compute foundation that enables scientific breakthroughs. Deliver reliable, secure platforms and self-service guardrails that accelerate experimentation and turn ideas into results - faster, at scale, and with confidence.

Your Responsibilities:

  • Architect, build, and operate our cloud platform, moving infrastructure beyond the initial setup to deliver resilient compute, network, and storage, including full-sized GPU clusters
  • Drive the implementation of highly structured, auditable delivery pipelines (CI/CD/GitOps) using to enforce automated, repeatable infrastructure changes
  • Design and deploy automated governance and security controls using Policy-as-Code (specifically Kyverno and YAML) to ensure strong isolation, protect data, and meet internal audit standards
  • Establish the foundational monitoring, alerting, and telemetry framework required for robust operations, defining clear SLOs, and setting the course for future SRE work
  • Partner with Research and Data teams to build self-service capabilities that efficiently support diverse workloads, from Python notebooks to distributed clusters

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