Principal Research Scientist, Synthetic Data Generation

NVIDIA•Us, CA

About The Position

NVIDIA is at the forefront of the AI revolution, and our research is shaping the future of large language models. We are looking for a Principal Scientist to set the technical direction for synthetic data generation across NVIDIA's frontier model efforts. You will define and build open-source libraries within the NVIDIA NeMo ecosystem that generate synthetic datasets across text, code, structured, and multimodal data, feeding the pre- and post-training of LLMs such as Nemotron. This role combines hands-on software engineering with applied research in generative methods, and you will collaborate with research, engineering, product, and model teams as well as external labs.

Requirements

PhD in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience.
15+ years of engineering and research experience in synthetic data generation, generative modeling, multimodal machine learning, or related areas.
Deep technical understanding of LLMs, how data shapes their pre-training, post-training, and RL stages, and inference frameworks such as vLLM or TGI.
Proven track record of developing or maintaining software libraries used by a broad developer community.
Experience building and optimizing scalable data pipelines for large-scale model training — throughput, distributed inference, and cost at cluster scale.
Strong publication record at premier venues such as NeurIPS, ICML, ICLR, ACL or similar.

Nice To Haves

Significant open-source contributions in ML or data tooling, with community adoption.
Experience with multimodal generation or understanding (vision-language, document AI, video, or audio).
Experience generating data for agentic, tool-use, or reinforcement-learning post-training, including RL environment design.
Background in differential privacy, de-identification, or synthetic data for regulated industries such as healthcare, finance, or government.
Experience influencing model training decisions at frontier scale, or partnering directly with pre-training and post-training teams.

Responsibilities

Build and scale data generation pipelines using LLM-based methods combined with automated quality evaluation, resulting in datasets to improve both initial training and fine-tuning of LLMs such as Nemotron. These data pipelines cover reasoning, coding, structured output, and multimodal understanding.
Pioneer data generation for agentic and tool-use training: synthetic trajectories, multi-turn interactions, function calling, and executable environments for reinforcement learning, including reward modeling and verifiable-reward data.
Advance multimodal synthetic data generation — image, document, video, and audio — in partnership with NVIDIA's model teams.
Advance privacy-preserving and safe synthesis — differential privacy, anonymization, and de-identification — enabling model training on sensitive data in regulated domains.
Develop and maintain open-source libraries and SDKs with clean APIs and strong documentation.
Drive software excellence with modern tooling, architecture based on configuration, and professional Git/CI-CD.
Publish original research at top machine learning and AI conferences to maintain NVIDIA's technical leadership.
Mentor scientists and engineers across the team, raising the technical bar and growing the next generation of researchers.