PhD Intern, Research - Model Alignment

Job Requisition ID #

25WD92256

Position Overview

Are you excited about advancing the next generation of intelligent design systems? Do you want to explore how reinforcement learning can make generative models more adaptive, diverse, and aligned with human creativity? If you're passionate about this, keep reading!

In this role, you will join Autodesk's Research team, focusing on advancing reinforcement learning for generative design models. You will develop and evaluate new algorithms that help models self-improve, learn from feedback, and generalize across diverse generation tasks. Working closely with senior research scientists, you'll prototype and test ideas that could lead to real-world impact in Autodesk products such as Fusion and AutoCAD, as well as publishable insights in the broader research community.

This is an exciting opportunity to contribute to Autodesk's mission of building AI that truly understands and amplifies human design intent, helping shape the future of creative automation.

Responsibilities

• Conduct research on self-play reinforcement learning (RL) methods for structured generative tasks
• Design and implement RL-based alignment frameworks using PyTorch, Ray, and RayLightning for distributed training
• Prototype and test algorithms that improve the validity, diversity, and stability of generated results
• Collaborate with Autodesk Research scientists and engineers to integrate learned models into real-world generative workflows
• Document findings and potentially contribute to a peer-reviewed publication (e.g., NeurIPS, ICLR, ICML)

• Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field
• Solid background in reinforcement learning, deep learning, language models, and generative modeling
• Proficiency with Python, PyTorch, and deep RL frameworks
• Understanding of distributed training or experience with Ray/RayLightning
• Strong analytical and communication skills; ability to interpret experimental results and iterate quickly

• Prior research experience in self-play RL, model alignment, or structured generative modeling (e.g., text, geometry, or graph domains)
• Familiarity with CAD representations and graph neural networks
• Publication or preprint experience in top-tier ML conferences (NeurIPS, ICLR, ICML, CVPR, RLC)
• Enthusiasm for bridging research and real-world generative design systems.
• Ability to work independently while collaborating across multidisciplinary teams