PhD Data Synthesis & User-Simulation Intern – Fall

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 over represent 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.

• 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, such as 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.

• Pursuing a PhD in Computer Science, Machine Learning, Computational Linguistics, Computational Neuroscience, or an 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., Hugging Face, vLLM, distributed training).
• Strong research background with publications at top‑tier AI, ML, or NLP conferences.

• 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.

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.

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.