Data Generation and User Simulation Research Intern — Fall 2026

NVIDIA•Santa Clara, CA

6d•$30 - $94

About The Position

NVIDIA is at the forefront of AI, driving the next era of computing where GPUs power intelligent systems. This role is within a research team focused on a critical challenge in modern model development: advanced artificial data creation for pre-training, post-training, and evaluation infrastructure. The team is investigating how generative models can create high-utility instructional and assessment data, measured by downstream model performance rather than surface plausibility. A key workstream is population-grounded user simulation, involving synthetic users interacting with LLMs, calibrated against real behavioral signatures, to yield training signals. Other areas include verifier-grounded trajectory synthesis, multilingual and low-resource coverage, and SDG quality measurement. This internship offers a chance to contribute to foundational research shaping the future of AI model training.

Requirements

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.

Nice To Haves

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.

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, including behavioral calibration of simulated users against real-user signatures, 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 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.