OpenAI의 Data Understanding 팀에서 멀티모달 데이터를 구축하고 정제할 연구원을 찾습니다. 머신러닝 연구 실적과 데이터 중심 AI에 대한 깊은 이해가 필수입니다. 이미지, 오디오, 비디오 등 대규모 데이터를 합성하고 처리하여 모델 성능을 최적화하는 핵심 역할을 수행하게 됩니다.
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.
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.
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.
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.
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