Software Engineer, GPU Infrastructure- Chatgpt Engineering

OpenAI OpenAI · AI Frontier · London, United Kingdom · Applied AI

Software Engineer focused on building and operating the GPU infrastructure for ChatGPT, including tooling, automation, and intelligent systems for scalability, observability, and efficiency. The role involves designing systems for fleet health, capacity planning, and operational automation to support frontier AI development.

What you'd actually do

  1. Design, build, and operate software that manages large-scale GPU infrastructure supporting ChatGPT inference.
  2. Build internal platforms, tooling, and AI-powered agents that automate fleet operations and reduce operational overhead.
  3. Improve observability, reliability, and operational efficiency across thousands of GPUs.
  4. Develop systems for capacity planning, scheduling, fleet health monitoring, and incident response.
  5. Identify infrastructure bottlenecks and implement solutions that improve utilization, scalability, and performance.

Skills

Required

  • 5+ years of software engineering experience building production infrastructure
  • Strong programming skills in Go, Python, C++, Rust, or similar systems languages
  • Experience designing and operating highly available distributed systems
  • Experience with GPU infrastructure, high-performance computing, ML infrastructure, or large-scale compute platforms
  • Experience with Kubernetes, cloud infrastructure, Linux, networking, and observability tooling
  • Excellent debugging, systems design, and operational problem-solving skills
  • Strong communication skills and experience collaborating across engineering organizations

Nice to have

  • Experience operating large-scale GPU clusters or other compute-intensive distributed systems
  • Background in Production Engineering, Site Reliability Engineering (SRE), Infrastructure Engineering, or Platform Engineering
  • Built software that automates operational workflows rather than relying on manual processes
  • Experience with Kubernetes, Linux systems, container orchestration, or distributed infrastructure
  • Understand infrastructure observability, monitoring, capacity planning, and incident management
  • Enjoy identifying cross-team pain points and building reusable platforms that improve developer productivity
  • Comfortable working across software engineering and systems operations, owning problems end-to-end
  • Thrive in fast-moving environments with significant technical ambiguity

What the JD emphasized

  • operating large-scale GPU or compute infrastructure
  • large-scale GPU infrastructure
  • large-scale production infrastructure
  • GPU infrastructure
  • large-scale compute platforms

Other signals

  • operating large-scale GPU clusters
  • inference workloads
  • AI-powered operational tooling
  • AGI