Synthetic Data Generation and User Simulation PhD Research Intern — Fall 2026

About the role

This is an opportunity to contribute to foundational research that will help shape how the next generation of AI models is trained.

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.
• 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.

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.

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.

Benefits

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!

Pay

NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Schedule

Applications for this job will be accepted at least until May 31, 2026. This posting is for an existing vacancy.