Data Generation and User Simulation Research Intern - Fall 2026

Responsibilities

• Researching innovative techniques in generative models, artificial data creation, user simulation, reward modeling, and data-quality estimation for LLM training.
• Conducting experiments to validate that your synthetic data measurably improves downstream model performance - accuracy, robustness, calibration, multilingual parity, agentic safety - rather than only matching surface statistics.
• Collaborating with other researchers and engineers to integrate novel methods into production training and evaluation pipelines.
• Preparing research findings for internal presentations and potential publication at top-tier AI conferences
• What we need to see:
• Pursuing a PhD in Computer Science, Machine Learning, Computational Linguistics, Computational Neuroscience, or equivalent program, with a specialization in deep learning, NLP, or LLM training.
• Research experience in at least one of: generative modeling, synthetic data generation, LLM post-training (SFT/RLHF/DPO/RL), reward modeling, multi-agent or interactive simulation, behavioral or cognitive modeling, or large-scale data curation.
• Excellent Python programming skills.
• Hands-on experience with deep learning frameworks (PyTorch) and the modern LLM training/serving stack (e.g., HuggingFace, vLLM, distributed training).
• Strong research background with publications at top-tier AI, ML, or NLP conferences.
• Ways to stand out from the crowd:
• Experience training or fine-tuning LLMs end-to-end and evaluating them against real downstream tasks.
• Prior work on LLM-as-judge calibration, inter-rater agreement, or evaluator robustness for subjective dimensions.
• Prior work on user simulation, agent-user interaction modeling, or behavioral modeling grounded in real population data or cognitive science.
• Interest or background in multilingual / low-resource / sovereign-AI evaluation and training.
• Contributions to open-source projects in the SDG, LLM training, or evaluation space.
• NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!
• Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 30 USD - 94 USD.
• You will also be eligible for Intern benefits .
• Applications for this job will be accepted at least until May 25, 2026. This posting is for an existing vacancy.
• NVIDIA uses AI tools in its recruiting processes.
• NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity emplo

Additional Information

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.

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