Senior Scientist, Synthetic Data Generation

at Nvidia

📍 Santa Clara, United States

USD 168,000-264,500 per year

SENIOR

✅ On-site

Tech Stack
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AI API @ 7 Git LLM @ 4 Machine Learning @ 4 Statistics @ 4 Technical Leadership vLLM @ 7

Details

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 Senior Scientist to join our team and help advance our capabilities in synthetic data generation for training frontier models. You will contribute to open-source libraries within the NVIDIA NeMo ecosystem that generate synthetic datasets across text, code, structured, and multimodal data, directly 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.

Responsibilities

Build synthetic data generation pipelines using LLM-based methods and automated quality evaluation, producing datasets that improve the pre- and post-training of LLMs such as Nemotron — reasoning, coding, structured output, and multimodal understanding.
Advance multimodal synthetic data generation — image, document, video, and audio — in partnership with NVIDIA's model teams.
Design 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 interns and junior researchers to develop technical growth within the team.

Requirements

PhD in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience.
A research background of 3+ years in synthetic data generation, generative modeling, multimodal machine learning, or related areas. Comparable experience is also considered.
Deep technical understanding of LLMs, how data shapes their pre- and post-training, and inference frameworks such as vLLM or TGI.
Proven track record of developing or maintaining software libraries used by a broad developer community.
Strong publication record at premier venues such as NeurIPS, ICML, ICLR, ACL or similar.