Principal Research Scientist, Synthetic Data Generation

NVIDIA

Principal scientist leading NVIDIA’s synthetic data generation for frontier LLM training. Building open-source NeMo tools across text, code, multimodal, agentic, and privacy-preserving datasets.

Posted 9/4/2026full-timeRemote • California, Colorado, Massachusetts, New York • 🇺🇸 United StatesLead💰 $272,000 - $431,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in synthetic data generation, multimodal machine learning, and large language models (LLMs), with a strong focus on developing open-source libraries and scalable data pipelines. Proven ability to mentor teams and publish research in leading AI and machine learning conferences.

Highest-signal resume keywords

PhD In Computer Science15+ Years Of Engineering ExperienceSynthetic Data GenerationMultimodal Machine LearningDifferential Privacy

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Synthetic Data GenerationGenerative ModelingLarge Language Models (LLMs)Data Pipeline OptimizationReinforcement Learning (RL)Open-Source Library DevelopmentAutomated Quality EvaluationMultimodal Data GenerationFunction CallingReward Modeling

Soft Skills

MentoringCollaboration

Tools & Technologies

NVIDIA NeMoGitCI-CDVLLMTGI

Industry Keywords

Machine LearningStatisticsHealthcareFinanceGovernment

About the role

Key responsibilities & impact

Set the technical direction for synthetic data generation across NVIDIA's frontier model efforts
Define and build open-source libraries within the NVIDIA NeMo ecosystem
Generate synthetic datasets across text, code, structured, and multimodal data for LLM pre- and post-training
Build and scale LLM-based data generation pipelines with automated quality evaluation
Develop synthetic trajectories, multi-turn interactions, function calling, executable environments, reward modeling, and verifiable-reward data for agentic and tool-use training
Advance multimodal synthetic data generation for image, document, video, and audio
Advance privacy-preserving and safe synthesis using differential privacy, anonymization, and de-identification
Develop and maintain open-source libraries and SDKs with clean APIs and strong documentation
Drive software excellence through modern tooling, configurable architecture, and professional Git/CI-CD
Publish original research at leading machine learning and AI conferences
Collaborate with research, engineering, product, model teams, and external labs
Mentor scientists and engineers across the team

Requirements

What you’ll need

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, 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, including 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, including 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

Comp & perks

Equity
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