The opportunity
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…
What you'll do
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).
What they're looking for
- Provide visualization and inspection tools to surface errors, bugs, or bottlenecks in datasets.
- 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.
