Software Engineer, Data Infrastructure - Research

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 foundational (if not glamorous) part of the ML stack. Bonus points if you: - Have background knowledge in data math, probability, or distributed data theory. - Have worked with GPU-scale distributed systems or dataset scaling for real-time data About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this lin

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 foundational (if not glamorous) part of the ML stack. Bonus points if you: Have background knowledge in data math, probability, or distributed data theory. Have worked with GPU-scale distributed systems or dataset scaling for real-time data

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.