PhD Data Generation and User Simulation Research Intern — Fall 2026

Summary: NVIDIA is a leading technology company focused on AI and computing innovation. They are seeking a PhD Data Generation and User Simulation Research Intern to contribute to foundational research in generative models and user simulation for LLM training, collaborating with researchers to validate synthetic data's impact on model performance. Responsibilities: - Researching innovative techniques in generative models, artificial data creation, user simulation, reward modeling, and data-quality estimation for LLM training - Crafting and applying new methods for high-fidelity synthetic data. For example, behavioral calibration of simulated users against real-user signatures. Also, procedurally generated probe and scenario coverage, trajectory generation guided by verification, process-reward extraction from multi-step interactions, and population-aware data mixing for pre-training and post-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 Required Qualifications: - 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 Preferred Qualifications: - 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 Required Skills: Generative modeling, Synthetic data generation, LLM post-training, Reward modeling, Multi-agent simulation, Behavioral modeling, Cognitive modeling, Large-scale data curation, Python programming, PyTorch, HuggingFace, vLLM, Distributed training, Research publication in AI/ML/NLP conferences Internship Start Date: Start in 2026 Fall Benefits: You will also be eligible for Intern benefits.