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Intern, Construction AI Agent Research (Winter 2026)
Autodesk
Design and make software for architecture, engineering, construction, and entertainment industries.
Develop multimodal AI agents for construction using LLMs, vision models, and graphs.
2d ago
Entry-level
Internship
Canada
Hybrid
Company Size
11,600 Employees
Service Specialisms
Design
Engineering
Construction
Architecture
Consulting
Product Development
Technology Solutions
Software Development
Sector Specialisms
Building Design
Construction
Automotive
Building Product Manufacturing
3D Animation
Architecture
Engineering
Construction Professionals
Role
What you would be doing
data analysis
graph querying
ai prototype
multimodal ai
agent integration
research reporting
Research and Implement Data Analysis Techniques: Explore graph querying methods and multimodal data to interpret and combine textual, visual, and sensor data from construction sources. Address key challenges, including handling domain-specific nuances in AEC data.
Prototype AI Solutions in Construction Project: Build a functional AI agent prototype, including demos that showcase capabilities in querying, data analysis, and inter-agent interactions in a real-world AEC project.
Multimodal AI Agent Development: Design and prototype AI agents using LLMs, vision-language models, and multi-agent frameworks. Focus on integrating agent protocols to enable robust interactions between agents and external tools (e.g., APIs, databases, and resources).
Conduct Experiments and Present Findings: Perform hands-on research on AI agent architectures for construction applications and present results to the team through reports, demos, or potential contributions to academic papers.
What you bring
phd candidate
python
llms
neo4j
pytorch
multimodal
Pursuing a Master's or PhD in Computer Science, Machine Learning/AI, Robotics, or a related field, with familiarity in AEC domains.
Excellent analytical, problem-solving, and communication skills, with a passion for innovative technology in construction.
Hands-on experience with LLMs, vision-language models (e.g., GPT-4V, Claude, Gemini), and graph tools (e.g., Neo4j, GraphQL, or similar).
Strong proficiency in Python and experience with AI frameworks such as LangChain, PyTorch, or similar for building agents and models.
Understanding of multimodal data processing, including techniques for combining textual, visual, and sensor inputs.
Solid grasp of agent protocols (e.g., Model Context Protocol, Agent2Agent Protocol) and experience with external tool integrations (e.g., APIs).
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