Principal Research Scientist, Synthetic Data Generation
NVIDIA· Research Staff Member
Hybrid (US, CA, Santa Clara), Remote, Hybrid (US, NY, New York)$272k - $431.3kFull-timePosted 1mo ago
About the role
Lead technical direction for synthetic data generation across NVIDIA's frontier model efforts, building open-source libraries in the NVIDIA NeMo ecosystem to generate synthetic datasets for text, code, structured, and multimodal data to support pre-training and fine-tuning of LLMs such as Nemotron. Combines hands-on software engineering with applied research in generative methods and collaborates with internal teams and external labs.
Responsibilities
- Build and scale data generation pipelines using LLM-based methods with automated quality evaluation to create datasets for initial training and fine-tuning of LLMs (e.g., Nemotron) across 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.
Qualifications
- 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, data shaping pre-training/post-training/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.
Skills
Required:LLM data pipeline designOpen-source software development
Nice to have:Multimodal data generationDifferential privacy / privacy-preserving techniques
Benefits
- Equity
- Benefits per NVIDIA policy
- Professional development opportunities
- Inclusive and diverse work environment
Last updated Sep 11, 2026. Interested? Apply on the company site.
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