Training, Process Management Engineer

Openai; Deutschland; Vor Ort; EUR 80.943 - 104.070; Vollzeit

Benefits dieser Stelle

Relocation assistance

Hybrid work model

Zusammenfassung

Openai is seeking a Training Runtime: Process Management Engineer based in London, UK. This role entails working on software that integrates thousands of computers into a cohesive system, ensuring stability and performance while supporting high-impact research and large-scale training runs.

You will primarily utilize Rust and Python, developing high-performance systems essential for orchestrating machine learning workloads, optimizing performance, and debugging distributed systems. The position offers a hybrid work model and relocation assistance.

Qualifikationen

• Experience with high-performance asynchronous systems and distributed architectures.
• Strong debugging skills in complex distributed systems.
• Ability to optimize both local and distributed performance.

Aufgaben

• Work across our Python and Rust stack.
• Design, build, and maintain software for machine learning workloads.
• Profile and optimize our software stack for computation orchestration.

Kenntnisse

Rust

Python

Distributed systems

High-performance architectures

System reliability

Jobbeschreibung

About 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 hundreds 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. You will work primarily in Rust, building high‑performance asynchronous systems with a strong emphasis on performance, correctness, and scalability. Working at this scale and at the frontier of AI development poses novel challenges. Out‑of‑the‑box approaches often don’t work. The problems you will be working on are highly ambiguous and require strong design judgment as well as proficient execution to advance the state of our infrastructure. We’re looking for people who love optimizing an end‑to‑end platform, understanding high‑performance architectures to maximize both local and distributed performance across our supercomputers. We’re looking for engineers excited by the rapid pace of responding to the dynamic and evolving needs of our training runtime and compute stack. This role is based in London, UK. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In This Role, You Will:

• Work across our Python and Rust stack
• Design, build, and maintain software to orchestrate and monitor machine learning workloads on our largest supercomputers
• Profile and optimize our software stack to support computation orchestration at frontier scale
• Improve reliability, observability, and fault tolerance for long‑running jobs
• Debug complex distributed systems issues across large clusters
• Respond to the changing shapes and needs of the ML systems to enable our r