About the job
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.
Responsibilities
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.
Qualifications
Minimum
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 foundational (if not glamorous) part of the ML stack.
Preferred
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 foundational (if not glamorous) part of the ML stack.