AI Infrastructure Engineer — Scale Training & Inference

OpenAI; United States; Remote; USD 150,000 - 190,000; Full time

Job summary

OpenAI is seeking an AI Systems Engineer to scale infrastructure for training and evaluation workflows. You will own projects from bottleneck analysis to deployment, merging distributed systems engineering with performance optimization, close collaboration with researchers, and platform development.

You will build and operate infrastructure for large-scale training and evaluation, develop shared inference and grading platforms with health monitoring, and improve scheduling to maximize GPU

Qualifications

• Experience building or operating large-scale distributed systems.
• Strong software engineering fundamentals.
• Ability to design, deploy, and operate infrastructure for ML workloads.

Responsibilities

• Build and operate infrastructure for large-scale training and evaluation, improving reliability, throughput, and resource efficiency.
• Develop shared inference and grading platforms with automated capacity management, health monitoring, and visibility into performance.
• Improve compute scheduling and resource allocation to reduce idle GPU time and help workloads recover quickly from failures.
• Diagnose bottlenecks across training, inference, and orchestration, and work across teams to improve end-to-end performance.
• Build self-service tools, automated validation, and observability that help researchers launch experiments, diagnose issues, and compare results with less manual intervention.

Skills

Distributed systems engineering

Performance optimization

Software engineering fundamentals

Research tooling

Job description

OpenAI is seeking an AI Systems Engineer to scale infrastructure for training and evaluation workflows. You will own projects from bottleneck analysis to deployment, merging distributed systems engineering with performance optimization, close collaboration with researchers, and platform development.

You will build and operate infrastructure for large-scale training and evaluation, develop shared inference and grading platforms with health monitoring, and improve scheduling to maximize GPU