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Software Engineer, Data Infrastructure - Research

OpenAI

Software Engineer, Data Infrastructure - Research is a still-open role at OpenAI on Jobrow. Location: On-site · San Francisco · California · United States. Listed pay: $250k–$380k. Sourced from the company's public Ashby board. First seen 2 months ago; last confirmed 9 days ago. Apply on the employer ATS — Jobrow is not the employer.

Pay
$250k–$380k
Workplace
On-site
Location
San Francisco · California · United States
First seen
2 months ago
Last seen
9 days ago
Board
Ashby
Apply

About this role

ABOUT THE TEAM The Workload team is responsible for designing and running OpenAI’s LLM training and inference infrastructure that powers frontier models at massive scale.

  • Function: Infrastructure
  • Seniority: mid

Location & pay

On-site · San Francisco · California · United States Listed pay on Jobrow: $250k–$380k.

Source

Jobrow reads the public Ashby board for OpenAI. First seen 2 months ago; last seen 9 days ago. We are not the employer — apply only on the company ATS.

Posting

ABOUT THE TEAM The Workload team is responsible for designing and running OpenAI’s LLM training and inference infrastructure that powers frontier models at massive scale. Our systems unify how researchers train and serve models, abstracting away the complexity of performance, parallelism, and execution across vast GPU/accelerator fleets. By providing this foundation, the Workload team ensures that researchers can focus on advancing model capabilities while we handle the scale, efficiency, and reliability required to bring those models to life. ABOUT THE ROLE We are looking for an engineer to design and implement the dataset infrastructure that powers OpenAI’s next-generation training stack. You will be responsible for building standardized dataset interfaces, scaling pipelines across thousands of GPUs, and proactively testing performance bottlenecks. In this role, you will collaborate closely with the multimodal researchers, and other infra groups to ensure datasets are unified, efficient, and easy to consume. IN THIS ROLE, YOU WILL: - Design and maintain standardized dataset APIs, including for multimodal (MM) data that cannot fit in memory. - Build proactive testing and scale validation pipelines for dataset loading at GPU scale. - Collaborate with teammates to integrate datasets seamlessly into training and inference pipelines, ensuring smooth adoption and a great user experience. - Document and maintain dataset interfaces so they are discoverable, consistent, and easy for other teams to adopt. - Establish safeguards and validation systems to ensure datasets remain reproducible and unchanged once standardized. - Debug and resolve performance bottlenecks in distributed dataset loading (e.g., straggler systems slowing global training). - Provide visualization and inspection tools to surface errors, bugs, or bottlenecks in datasets. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have strong engineering fundamentals with experience in distributed systems, data pipelines, or infrastructure. - Have experience building APIs, modular code, and scalable abstractions, while recognizing that abstractions ultimately serve the users and UX is an important part of the abstractions design. - Are comfortable debugging bottlenecks across large fleets of machines. - Take pride in building infrastructure that “just works,” and find joy in being the guardian of reliability and scale. - Are collaborative, humble, and excited to own a foundatio

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