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Software Engineer, ChatGPT Infrastructure

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EMP:Technology
London, UK
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the TeamOur London-based team builds the backend systems that help ChatGPT scale reliably

We work on infrastructure close to the product, partnering with engineering teams to improve the performance, resilience, and operability of critical user-facing systems.

Our work combines backend software engineering with distributed systems and production reliability.

We build shared capabilities, improve high-traffic workflows, and make it easier to introduce new product functionality without compromising performance or availability.

About the RoleThis role is for software engineers who want to build and evolve backend systems operating at significant scale.

You’ll write production code, design shared infrastructure, and solve technical challenges involving performance, distributed systems, and system reliability.

You’ll also own how those systems behave in production: how changes are rolled out, how issues are detected and diagnosed, and how recurring operational problems can be addressed through better software and system design.

This is a strong fit for backend engineers who enjoy complex systems problems and want a direct connection between the infrastructure they build and the experience of ChatGPT users.

In this role, you will:Design, build, and maintain backend systems supporting high-traffic ChatGPT experiences.

  • Develop shared services, APIs, and infrastructure that help product teams build and launch new capabilities safely.

Improve the performance, scalability, and efficiency of production systems as usage and product complexity grow.

Build and improve systems for asynchronous processing and other large-scale backend workloads.

  • Lead architectural improvements and infrastructure migrations while maintaining correctness, compatibility, and safe rollout and rollback.

Strengthen monitoring, alerting, and diagnostics to detect problems early and reduce customer impact.

Participate in on-call, incident response, and root-cause analysis, and turn operational learnings into.

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