Senior Applied Research Scientist, Data Curation

NVIDIA•Us, CA

3d•Remote

About The Position

NVIDIA's Curator team is seeking a Senior Applied Research Scientist with experience researching, developing, and deploying deep learning models at scale across a range of modalities. You’ll join a team of Applied Research Scientists, Machine Learning and MLOps Engineers working on the next generation of data curation and document extraction pipelines for foundation-model training, including capabilities that power NVIDIA Nemotron Parse, with a focus on structured content extraction, data quality and deduplication at the petabyte-scale. At NVIDIA we’re building the foundations upon which modern LLMs are trained. Our work is a critical component in the training of Nemotron LLM models which lead the field of open LLMs. Come be a part of our world-class team building the future of Curation and Retrieval.

Requirements

Candidates with a Master's, Ph.D. or equivalent experience in data curation, document AI, information retrieval or multimodal research, along with a track record of publication in leading conferences like 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.
An understanding of the state of the art in data curation research, with a focus on multimodal content extraction and deduplication.
10+ years of experience developing multimodal systems across a range of models and platforms.
Proven expertise managing distributed data frameworks like Ray, Spark, or Dask, coupled with a history of deploying massive, multi-node machine learning or data processing tasks within production environments.
Knowledge of best practices in batching, streaming, and scaling of ingestion pipelines to support real-world applications.
Excellent Python programming skills and a strong hands-on experience with PyTorch or comparable modern deep learning frameworks.
An ability to share and communicate your ideas clearly through blog posts, papers, kernels, GitHub, etc.
Excellent communication and interpersonal skills are required, along with the ability to work in a dynamic, user-focused, distributed team.

Nice To Haves

Kaggle Grandmaster status or a strong record of top-tier results in machine learning competitions is a strong plus.
Information retrieval experience is a big plus.
A history of mentoring junior engineers and interns is a plus.

Responsibilities

Working with our team of researchers to develop efficient and performant models and data pipelines that extract and curate multi-modal data (documents, image, audio and videos) used in the training of foundation models.
Building pipelines for petabyte-scale extraction and content deduplication, including document and html parsing, fuzzy and near-duplicate deduplication, semantic deduplication, and substring deduplication.
Contributing to the expansion and optimization of curation methodologies targeting petabyte-scale multimodal data run across hundred-node GPU clusters to improve the quality of foundation model training sets.
Exploring and crafting datasets, metrics, experiments, and validation scripts to develop standard methodologies for research. These methodologies will offer customers clear guidance on which models and pipelines to apply in specific contexts.
Helping ML Engineers scale pipelines to production capability through the development of NVIDIA Inference Microservices (NIMs) and blueprints which demonstrate how to deploy NIMs in a pipeline effectively.
Writing papers, blog posts, documentation and training materials that help customers understand and take advantage of our research.
Keeping up to date with the latest developments in data curation across academia and industry.