Training Performance Engineer

Openai; Deutschland; Vor Ort; EUR 103.146 - 137.528; Vollzeit

Benefits dieser Stelle

Relocation assistance

Hybrid work model

Zusammenfassung

OpenAI is hiring a Training Performance Engineer to enhance the efficiency of our distributed training stack. This role involves analyzing large-scale training runs and designing optimizations for peak performance across our training infrastructure.

The ideal candidate will have expertise in performance engineering and a strong programming background in Python and C++. This position operates under a hybrid work model, requiring three days onsite per week in San Francisco, with relocation assistance available.

Qualifikationen

• Strong programming skills in Python and C++. Rust or CUDA knowledge is a plus.
• Experience with performance optimization in large-scale systems.
• Ability to analyze GPU kernel performance and collective communication throughput.

Aufgaben

• Profile end-to-end training runs to identify performance bottlenecks.
• Optimize GPU utilization and throughput for model training.
• Collaborate with engineers to improve kernel efficiency.

Kenntnisse

Performance optimization

Python programming

C++ programming

Distributed training

Jobbeschreibung

About the Team

Training Runtime designs the core distributed machine‑learning training runtime that powers everything from early research experiments to frontier‑scale model runs. With a dual mandate to accelerate researchers and enable frontier scale, we’re building a unified, modular runtime that meets researchers where they are and moves with them up the scaling curve.

Our work focuses on three pillars: high‑performance, asynchronous, zero‑copy tensor and optimizer‑state‑aware data movement; performant, high‑uptime, fault‑tolerant training frameworks (training loop, state management, resilient checkpointing, deterministic orchestration, and observability); and distributed process management for long‑lived, job‑specific and user‑provided processes.

We integrate proven large‑scale capabilities into a composable, developer‑facing runtime so teams can iterate quickly and run reliably at any scale, partnering closely with model‑stack, research, and platform teams. Success for us is measured by raising both training throughput (how fast models train) and researcher throughput (how fast ideas become experiments and products).

About the Role

As a Training Performance Engineer, you’ll drive efficiency improvements across our distributed training stack. You’ll analyze large‑scale training runs, identify utilization gaps, and design optimizations that push the boundaries of throughput and uptime. This role blends deep systems understanding with practical performance engineering — analyzing GPU kernel performance, collective communication throughput, investigating I/O bottlenecks, and sharding our models so we can train them at massive scale.

You’ll help ensure that our clusters are running at peak performance, enabling OpenAI to train larger, more capable models with the same compute budget.

This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Profile end‑to‑end training runs to identify performance bottlenecks across compute, communication, and storage.
• Optimize GPU utilization and throughput for large‑scale distributed model training.
• Collaborate with runtime and systems engineers to improve kernel efficiency, scheduling, and collective communication performance.
• Implement model graph transforms to improve end‑to‑end throughput.
• Build tooling to monitor and visualize MFU, throughput, and uptime across clusters.
• Partner with researchers to ensure new model architectures scale efficiently during pre‑training.
• Contribute to infrastructure decisions that improve reliability and efficiency of large training jobs.

You might thrive in this role if you:

• Love optimizing performance and digging into systems to understand how every layer interacts.
• Have strong programming skills in Python and C++ (Rust or CUDA a plus).
• Have experience running distributed training jobs on multi‑GPU systems or H