AI Infrastructure Engineer: Scale Training & Systems

Precision Labs; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

OpenAI is seeking an AI Systems Engineer to scale the infrastructure behind our training and evaluation workflows. You’ll own projects from bottleneck identification to deployment, working across distributed systems, performance optimization, and collaboration with researchers.

You’ll build shared platforms, optimize scheduling and resource usage, and create observability tools to accelerate research while maintaining reliability at scale.

Qualifications

• Experience building and operating large-scale distributed systems.
• Strong software engineering fundamentals.
• Experience in ML infrastructure, inference systems, or infrastructure tooling.
• Ability to own troubleshooting from bottlenecks to deployment.
• Comfort with cross-team collaboration.

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.
• Diagnose bottlenecks across training, inference, and orchestration and coordinate across teams to improve end-to-end performance.
• Build self-service tools and observability that help researchers launch experiments, diagnose issues, and compare results with less manual intervention.

Skills

Distributed systems

Performance optimization

Large-scale infrastructure

Research tooling

Observability

Job description

OpenAI is seeking an AI Systems Engineer to scale the infrastructure behind our training and evaluation workflows. You’ll own projects from bottleneck identification to deployment, working across distributed systems, performance optimization, and collaboration with researchers.

You’ll build shared platforms, optimize scheduling and resource usage, and create observability tools to accelerate research while maintaining reliability at scale.