Senior Applied Research Scientist, Data Curation
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Job Description
Senior Applied Research Scientist developing NVIDIA’s petabyte-scale multimodal data-curation and deduplication pipelines. Advancing document extraction for foundation-model training and NVIDIA Nemotron.
Responsibilities:
- Develop efficient and performant models and data pipelines that extract and curate multimodal data—documents, images, audio, and videos—for foundation-model training
- Build petabyte-scale extraction and content-deduplication pipelines, including document and HTML parsing, fuzzy and near-duplicate deduplication, semantic deduplication, and substring deduplication
- Expand and optimize curation methodologies for petabyte-scale multimodal data across hundred-node GPU clusters
- Explore and craft datasets, metrics, experiments, and validation scripts to develop standard research methodologies
- Help ML Engineers scale pipelines to production through NVIDIA Inference Microservices (NIMs) and blueprints
- Write papers, blog posts, documentation, and training materials for customers
- Keep up to date with data-curation developments in academia and industry
- Collaborate with Applied Research Scientists, Machine Learning Engineers, and MLOps Engineers
- Mentor junior engineers and interns
Requirements:
- Master's, Ph.D. or equivalent experience in data curation, document AI, information retrieval or multimodal research
- Track record of publication in leading conferences such as CVPR, ICCV, ECCV, KDD, etc.
- Hands-on experience developing computer vision and document-extraction models and pipelines, including layout analysis, OCR, and table, figure, or formula extraction
- Kaggle Grandmaster status or a strong record of top-tier results in machine learning competitions is a strong plus
- Understanding of the state of the art in data curation research, focused on multimodal content extraction and deduplication
- 10+ years of experience developing multimodal systems across a range of models and platforms
- Information retrieval experience is a big plus
- Expertise managing distributed data frameworks such as Ray, Spark, or Dask
- History of deploying massive, multi-node machine learning or data processing tasks within production environments
- Knowledge of batching, streaming, and scaling ingestion pipelines
- Excellent Python programming skills
- Strong hands-on experience with PyTorch or comparable modern deep learning frameworks
- Ability to communicate ideas through blog posts, papers, kernels, GitHub, etc.
- Excellent communication and interpersonal skills
- Ability to work in a dynamic, user-focused, distributed team
Benefits:
- Highly competitive salary
- Equity
- Comprehensive benefits package
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