Why This Job is Featured on The SaaS Jobs
This role stands out in the SaaS ecosystem because it sits at the data layer that increasingly differentiates AI-enabled products. The remit spans multimodal dataset creation, representation, and large-scale preparation, work that influences model capability long before application features are built. In a market where many SaaS teams consume foundation models as a dependency, this position is closer to the upstream infrastructure that determines quality, reliability, and cost of AI systems.
From a career perspective, it offers durable SaaS-adjacent skills in building production-grade data pipelines, quantization workflows, and evaluation loops that tie data changes to measurable model outcomes. The emphasis on filtering, deduplication, quality control, and automation reflects the operational realities of shipping AI at scale, where repeatability and governance matter as much as novel ideas. Experience here tends to transfer to data-centric ML roles across AI product companies, applied research groups, and platform teams.
It is best suited to professionals who like owning ambiguous problems end to end, balancing research intuition with engineering discipline. The role fits those motivated by empirical iteration, careful measurement, and collaboration across data, modeling, and systems, rather than narrow feature delivery.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About The Team
The Data Understanding team is responsible for creating the high quality datasets and their quantized representation for OpenAI. This includes synthesizing multimodal 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 prepares, curates, synthesizes and understands multimodal data at scale. You’ll work on research and production problems like synthesizing multimodal content (images, audio, and video) and their supervisions, improving noisy data pipelines, building better quality filters, using models to automate data prep, and measuring whether changes in the dataset improve model performance.
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 multimodal data problems to carrying long-running work through to impact.
Be excited by OpenAI’s empirical, collaborative approach to research.
Nice To Have
Experience with multimodal learning, audio, vision, video, synthetic data, or data-centric ML.
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.
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