AI Training Infrastructure Engineer

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

Job summary

United States Digital Space LLC is seeking an AI Systems Engineer to scale infrastructure behind training and evaluation workflows. You will own projects from bottleneck identification to deployment, collaborating with researchers to improve reliability, throughput, and resource efficiency.

The role combines distributed systems engineering, performance optimization, and building self-service tools to empower researchers to launch experiments with minimal manual intervention.

Qualifications

• Experience building or operating large-scale distributed systems.
• Experience in ML infrastructure, inference systems, GPU performance, or infrastructure tooling.

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

Software engineering

ML infrastructure

GPU performance

Job description

United States Digital Space LLC is seeking an AI Systems Engineer to scale infrastructure behind training and evaluation workflows. You will own projects from bottleneck identification to deployment, collaborating with researchers to improve reliability, throughput, and resource efficiency.

The role combines distributed systems engineering, performance optimization, and building self-service tools to empower researchers to launch experiments with minimal manual intervention.