Training, Process Management Engineer
OpenAI · London, United Kingdom · Scaling
About this role
OpenAI is hiring a mid-level Technical Trainer in the services function based in London, United Kingdom (hybrid). The posting calls out experience with Linux, Python, Rust, Distributed Systems.
- Role
- Technical Trainer
- Function
- services
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- London, United Kingdom
- Work mode
- Hybrid
- Department
- Scaling
- Posted
- Mar 9, 2026
More roles at OpenAI
Job description
from OpenAI careersAbout the Team
Training Runtime designs the core distributed runtime that powers everything from early research experiments to frontier-scale model runs. We work on building robust, scalable, high performance components to support our distributed training workloads. Our priorities are to maximize the productivity of our researchers and our hardware, with the goal of accelerating progress towards AGI.
Within Training Runtime, the Process Management team develops the distributed OS responsible for launching, coordinating, and supervising the large numbers of processes that make up modern training workloads. Our runtime sits beneath training frameworks and on top of research infrastructure, ensuring jobs run reliably across massive clusters while maintaining performance, stability, and observability.
Success for us is measured by both system reliability and researcher velocity - enabling ideas to scale from experiments to production training runs.
About the Role
As a Training Runtime: Process Management Engineer, you will work on the software that ties thousands of computers together and exposes them as a unified system.
This system has to serve individual researchers running multiple parallel experiments, as well as our largest training runs spanning 100’s of thousands and even millions of machines and accelerators. This requires easy to use, introspectable systems that can promote a fast debugging and development cycle, as well as relentless optimization for scale while maintaining stability and performance throughout.