RE/RS, Data Understanding - Foundations

About The Team: The Data Understanding team is responsible for creating the high quality datasets and their quantized representation for OpenAI. This includes synthesizing data, building VQ representations, and processing, filtering, deduplication, quality control, and tokenization so it can be used effectively in big model training runs. About The Role: We're looking to advance how OpenAI builds and understands pretraining data at scale. You'll treat data quality and curation as core research problems: developing new methods to select, combine, and transform data; creating datasets that improve model capabilities; and designing rigorous experiments to understand how data choices and interventions affect model learning and downstream behavior. You'll work closely with frontier models and web-scale data to build evidence for which approaches work and why, then translate successful research into scalable data processing pipelines.

Responsibilities

Advance how OpenAI builds and understands pretraining data at scale; Treat data quality and curation as core research problems; Develop new methods to select, combine, and transform data; Create datasets that improve model capabilities; Design rigorous experiments to understand how data choices and interventions affect model learning and downstream behavior; Work closely with frontier models and web-scale data to build evidence for which approaches work and why; Translate successful research into scalable data processing pipelines.

Requirements

Have a strong track record of new or improved ML ideas, through publications, projects, or applied research; Own and drive a research agenda, from choosing the right problems to carrying long-running work through to impact; Be excited by OpenAI’s empirical, collaborative approach to research.

Nice to have

Thoughtfulness about AI’s impact, including privacy, provenance, and data quality; Experience building high-performance deep learning or large-scale data processing systems.