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 We Expect You To - 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. 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 link. OpenAI Global Applicant Privacy Policy At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

We Expect You To: 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.

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; and design rigorous experiments to understand how data choices and interventions affect model learning and downstream behavior. Build evidence for which approaches work and why, then translate successful research into scalable data processing pipelines.