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US, CA, Santa Clara, US

Internship

Onsite

$30/hr - $94/hr

Intern

NVIDIA is a leading technology company harnessing the power of AI to redefine computing. The Data Generation and User Simulation Research Intern will engage in groundbreaking research on generative models and artificial data creation, contributing to the advancement of AI model training through innovative user simulations.

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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

Qualification

Required

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

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