Principal Research Scientist, Synthetic Data Generation
$272k - $431k • Remote • US, CA, Santa Clara
Posted 1mo ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Research Engineer
About the job
NVIDIA is seeking a Principal Scientist to lead the technical direction for synthetic data generation across its frontier model initiatives. This role involves defining and building open-source libraries within the NVIDIA NeMo ecosystem to create synthetic datasets for text, code, structured, and multimodal data. These datasets will be crucial for the pre- and post-training of large language models like Nemotron. The position blends hands-on software engineering with applied research in generative methods, requiring collaboration with various internal teams and external research labs.
Responsibilities
- Build and scale data generation pipelines using LLM-based methods and automated quality evaluation for LLM training and fine-tuning.
- Pioneer data generation for agentic and tool-use training, including synthetic trajectories and reinforcement learning environments.
- Advance multimodal synthetic data generation for image, document, video, and audio data.
- Develop and implement privacy-preserving and safe synthesis techniques for sensitive data.
- Develop and maintain open-source libraries and SDKs with clear APIs and documentation.
- Drive software excellence using modern tooling, configuration-based architecture, and CI/CD practices.
- Publish original research at top machine learning and AI conferences.
- Mentor scientists and engineers to enhance technical capabilities and foster future researchers.
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, or multimodal machine learning.
- Deep technical understanding of LLMs, their training stages, and inference frameworks like vLLM or TGI.
- Proven track record of developing or maintaining widely-used software libraries.
- Experience building and optimizing scalable data pipelines for large-scale model training.
- Strong publication record at premier AI/ML conferences (NeurIPS, ICML, ICLR, ACL, etc.).
Benefits
- Equity
- Health insurance
- Paid time off