[Remote] Principal Research Scientist, Synthetic Data Generation

Note: The job is a remote job and is open to candidates in USA. NVIDIA is a technology company advancing artificial intelligence and large language model research. The Principal Research Scientist will set the technical direction for synthetic data generation, build scalable generation pipelines and open-source libraries, and support pre-training, post-training, and reinforcement learning for frontier models. The role also involves multimodal data generation, privacy-preserving synthesis, research publication, and mentoring scientists and engineers.

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

Skills

• 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
• 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

Benefits

• Equity
• Benefits

Company Overview

Company H1B Sponsorship