Lead Research Scientist - Synthetic Data for LLMs

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 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.