Research Infrastructure Engineer, Training Systems
About The Team The team works on research and systems that advance frontier models. Our work often goes beyond standard training recipes, which means we also build the infrastructure needed to make new training approaches practical at scale.
What this role actually needs.
About The Team The team works on research and systems that advance frontier models. Our work often goes beyond standard training recipes, which means we also build the infrastructure needed to make new training approaches practical at scale. Responsibilities: - Build and maintain infrastructure for large-scale model training and experimentation. - Design APIs and interfaces that make complex training workflows easier to express and harder to misuse. - Improve reliability, debuggability, and performance across training and data pipelines. - Debug issues spanning Python, PyTorch, distributed systems, GPUs, networking, and storage. - Write tests, benchmarks, and diagnostics that catch meaningful regressions. - You want to build systems that enable new model training approaches, not just optimize established ones. 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 Research Infrastructure Engineer, Training Systems role.
- Build and maintain infrastructure for large-scale model training and experimentation.
- Design APIs and interfaces that make complex training workflows easier to express and harder to misuse.
- Improve reliability, debuggability, and performance across training and data pipelines.
- Debug issues spanning Python, PyTorch, distributed systems, GPUs, networking, and storage.
- Write tests, benchmarks, and diagnostics that catch meaningful regressions.
- You want to build systems that enable new model training approaches, not just optimize established ones.
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United States tech market
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Source: jobs.ashbyhq.com · Source ID: 7bfc114e-7eca-4de8-a8b6-0bcff1daed73 · Confidence: 97/100 · Last checked: May 7, 2026
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