RE/RS, Data Understanding - Foundations

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

Treat data quality and curation as core research problems; Developing new methods to select, combine, and transform data; Creating datasets that improve model capabilities; Designing rigorous experiments to understand how data choices and interventions affect model learning and downstream behavior; Working closely with frontier models and web-scale data to build evidence for which approaches work and why; Translating 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.