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.
• Conducting experiments to validate that your synthetic data measurably improves downstream model performance.
• 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.

Skills

• Deep learning and generative modeling expertise.
• Experience with synthetic data generation and user simulation.
• Knowledge of reward modeling and data-quality estimation for LLM training.
• Ability to conduct rigorous experimental validation of synthetic data.
• Collaboration and integration skills with engineering teams.
• Effective communication and presentation skills for research findings.

Benefits

• Standard pay based on the position, your location, year in school, degree, and experience.
• Intern benefits.

Pay

• Hourly rate for interns is 30 USD - 94 USD.

Schedule

• Full-time internship.