Principal Research Scientist, Synthetic Data Generation

NVIDIA Corporation; New York (NY); On-site; USD 272,000 - 431,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA Corporation is seeking a Principal Scientist to set the technical direction for synthetic data generation across frontier model efforts. You will build open-source libraries within the NeMo ecosystem to generate datasets across text, code, structured, and multimodal data for pre- and post-training and RL stages.

The role combines hands-on software engineering with applied research in generative methods, collaborating with researchers, engineers, product teams, and external labs.

Qualifications

• PhD or equivalent experience in CS/ML/stats.
• 15+ years of engineering and research in synthetic data generation or related areas.
• Deep technical understanding of LLMs and data shaping for pre-/post-training and RL.
• 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.

Responsibilities

• Define and build open-source libraries within NVIDIA's NeMo ecosystem for synthetic data generation across text, code, structured, and multimodal data.
• Develop data generation pipelines using LLM-based methods with automated quality evaluation.
• Advance privacy-preserving synthesis, including differential privacy and de-identification.
• Publish original research at top ML/AI conferences and mentor team members.

Skills

LLM-based data generation

Multimodal data generation

Differential privacy

Open-source libraries

Git/CI-CD

Education

PhD in Computer Science or related

Tools

vLLM

TGI

Job description

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.

What you'll be doing:

• 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.

What we need to see:

• 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.

Ways to stand out from the crowd:

• 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.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward‑thinking and talented people in the world working with us.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD. You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until September 8, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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