RE/RS, Data Understanding - Foundations

📋 Description

• Advance how OpenAI builds and understands pretraining data at scale.
• Treat data quality and curation as core research problems: developing methods to select, transform data.
• Create datasets that improve model capabilities; design rigorous experiments to understand 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, then 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.
• Thoughtfulness about AI’s impact, including privacy, provenance, and data quality.
• Experience building high-performance deep learning or large-scale data processing systems.