Position Overview
As an Intern in Autodesk Research's AI Lab, you will explore how large language models (LLMs) and intelligent multi-agent systems can enable advanced reasoning in architecture, engineering, and construction (AEC).
You'll help design AI systems that reason over complex, multimodal design problems — integrating spatial, quantitative, textual, and procedural knowledge — to make the built world smarter, safer, and more sustainable.
Our team combines expertise in machine learning, computational design, simulation, and AEC workflows. This project focuses on developing methods for structured reasoning and collaboration among AI agents, inspired by how human experts coordinate across design, engineering, and planning tasks. The goal is to advance AI systems that can analyze, critique, and generate design alternatives grounded in real-world AEC contexts. Autodesk's AI Lab actively publishes at NeurIPS, ICML, ICLR, and CVPR, and collaborates with leading academic and industry partners. Interns join a vibrant research community combining academic freedom with direct product impact.
Responsibilities
- Develop and execute a research agenda focused on advancing AI reasoning and multimodal learning in architecture, engineering, and construction (AEC)
- Create and refine cutting-edge algorithms, drawing from multiple aproaches in artificial intelligence
- Collaborate with researchers and engineers across diverse disciplines, including communicating research plans, progress, and results
- Explore methods for structured reasoning and collaborative problem-solving among intelligent systems
- Shape the future of Autodesk's architecture, engineering, and construction products through published research and practical applications
Minimum Qualifications
- Full-time student pursuing an MS or PhD (preferred) in Computer Science, Artificial Intelligence, Computational Design, Applied Mathematics, or a related field
- Broad understanding of machine learning, deep learning, and reasoning systems
- Strong programming skills in Python, with proficiency in PyTorch or TensorFlow
- Demonstrated experience in conducting, analyzing, and communicating research
- Excellent written and verbal communication skills, and ability to collaborate in cross-disciplinary teams
Preferred Qualifications
- Expertise in an area such as natural language processing, computer vision, 2D/3D geometry representation learning, reinforcement learning, graph neural networks, unsupervised/self-supervised learning, meta-learning, and/or generative models
- Strong command of modern AI, including foundation models or multimodal models
- Distinguished research achievements, such as intellectual property filings, awards, and leading authorship in top-tier venues (including but not limited to major ML/AI conferences, workshops, and journals)
- Demonstrated programming experience through professional roles, internships, hackathons, or contributions to open-source repositories (e.g., GitHub)
- Experience wrangling large, complex, high-dimensional data for large-scale training
- Proven ability to tackle complex challenges independently
- Passion for bridging AI reasoning with real-world AEC design and simulation workflows
