Description
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.
The Data Understanding team is responsible for creating 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.
We expect you to have a strong track record of new or improved ML ideas, through publications, projects, or applied research. You should own and drive a research agenda, from choosing the right multimodal data problems to carrying long-running work through to impact. You should 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.