NVIDIA
SyntheticDataGenerationandUserSimulationPhDResearchIntern—Fall2026
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“Synthetic Data Generation and User Simulation PhD Research Intern — Fall 2026 at NVIDIA. Skills: generative models, synthetic data generation, user simulation, LLM training. Research innovative techniques. Craft and apply new methods”
What You'll Achieve.
measurably improves downstream model performance
Industry & Context.
investigating how generative models can create instructional and assessment data; grounding data in real-world distributions; population-grounded user simulation; verifier-grounded trajectory synthesis; SDG quality measurement
What They're Looking For.
Must Have
PhD in Computer Science, Machine Learning specialization, Computational Linguistics specialization, Computational Neuroscience specialization, deep learning specialization, NLP specialization, LLM training specialization, Research experience in generative modeling, Research experience in synthetic data generation, Research experience in LLM post-training, Research experience in reward modeling, Research experience in multi-agent simulation, Research experience in interactive simulation, Research experience in behavioral modeling, Research experience in cognitive modeling, Research experience in large-scale data curation, Python programming skills, deep learning frameworks experience, modern LLM training stack experience, modern LLM serving stack experience, research background with publications
Nice to Have
Experience training LLMs end-to-end, Experience fine-tuning LLMs end-to-end, Evaluating LLMs against real downstream tasks, LLM-as-judge calibration experience, Inter-rater agreement experience, Evaluator robustness experience, User simulation experience, Agent–user interaction modeling experience, Behavioral modeling experience grounded in real population data, Behavioral modeling experience grounded in cognitive science, Interest in multilingual evaluation, Interest in low-resource evaluation, Interest in sovereign-AI evaluation, Interest in multilingual training, Interest in low-resource training, Interest in sovereign-AI training, Contributions to open-source projects
What You'll Do.
Research innovative techniques
Craft and apply new methods
Collaborate with researchers and engineers
Prepare research findings
How You'll Work.
Team & Collaboration
Collaborating with other researchers and engineers
Communication Scope
internal presentations
Full Job Description
Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world. We're a research team dedicated to a major challenge in modern model development. It involves advanced artificial data creation across pre-training, post-training, and evaluation infrastructure. Collecting only real data at scale carries meaningful quality, cost, latency, and privacy tradeoffs; it tends to overrepresent certain populations; and it often leaves gaps on the long tail of languages, domains, demographics, and safety scenarios. We're investigating how generative models can create instructional and assessment data that shows high utility. The measurement is based on downstream model performance instead of surface plausibility. Additionally, we explore grounding that data in real-world distributions to ensure it generalizes. A major workstream within this agenda is population-grounded user simulation: synthetic users interacting with LLMs, calibrated against real behavioral signatures, and structured to yield training signals (SFT examples, preference pairs, verifier corpora, process reward models, on-policy RL environments). Other examples include verifier-grounded trajectory synthesis where ground truth exists, multilingual and low-resource coverage, and SDG quality measurement across pre- and post-training corpora. This is an opportunity to contribute to foundational research that will help shape how the next generation of AI models is trained. **What you 'll be doing:** * Researching innovative techniques in generative models, artificial data creation, user si
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