AI Infrastructure Engineer, Pagi
About the Team pAGI Infra team builds and operates the systems that make large-scale model training and evaluation reliable, efficient, and easy to run. Our work spans distributed training infrastructure, inference and grading platforms, compute scheduling, and research tooling.
What this role actually needs.
About the Team pAGI Infra team builds and operates the systems that make large-scale model training and evaluation reliable, efficient, and easy to run. Our work spans distributed training infrastructure, inference and grading platforms, compute scheduling, and research 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. - Are excited about the potential of personal AGI and want to build the infrastructure that enables it. Company context: OpenAI builds frontier AI systems, research infrastructure, and applied products for developers, enterprises, and global users.
Day-to-day expectations
OpenAI lists these responsibilities for the AI Infrastructure Engineer, Pagi role.
- 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.
- Are excited about the potential of personal AGI and want to build the infrastructure that enables it.
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United States tech market
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Compensation read
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Hybrid roles in San Francisco should be compared against commute, local salary bands, and nearby employer demand.
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Source: jobs.ashbyhq.com · Source ID: b2250643-bfd0-4ce6-abbf-cb7e8c8123ba · Confidence: 97/100 · Last checked: Oct 12, 2026
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