As part of Autodesk’s team, this internship role may have the opportunity to contribute to project(s) supporting Autodesk’s partnership as the Official Design and Make Platform of the LA28 Olympic and Paralympic Games and Team USA. 

Position Overview 

 Autodesk is offering a graduate-level research internship for students interested in the intersection of sustainability, building design, and artificial intelligence. This position focuses on critically evaluating and advising the development of an AI-driven workflow that supports early-stage building design decisions related to material selection and embodied carbon performance. The primary application of this workflow is to support Autodesk’s work with the LA28 Games. 

The intern will contribute expert academic insight to ensure that AI agents: 

• Formulate building design problems appropriately 

• Leverage environmental datasets correctly 

• Produce outputs that are accurate, relevant, and aligned with real-world design practice 

This role is designed for students seeking applied research experience, exposure to industry-scale AI workflows, and opportunities to translate LCA academic methods into practical tools. 

Academic Value & Outcomes 

• Apply academic research methods to an industry AI development context 

• Gain experience evaluating, shaping and validating AI-driven sustainability tools 

• Collaborate with interdisciplinary teams spanning sustainability, software, and AI research 

• Opportunity to contribute to internal white papers, methodologies, or future publications 

• Exposure to research that supports Autodesk role as the Official Design and Make Platform of the LA28 Games 

This role is well suited for graduate students who are interested in bridging research and practice, have strong opinions about data quality, assumptions, and methodological rigor, and want to influence how AI systems reason about sustainability in the built environment.  

Responsibilities 

Phase 1: Problem Framing & Data Foundations 

• Review AI workflow documentation, preliminary outputs, and customer feedback 

• Evaluate how AI agents define building design problems and scope sustainability-related inquiries 

• Assess the use of environmental datasets (e.g., EPDs, carbon databases) for accuracy, relevance, and methodological alignment 

• Provide recommendations for improved data sources, assumptions, or research framing 

Phase 2: Workflow Development & Assessment Quality 

• Advise on workflow refinements to improve AI reasoning, transparency, and output reliability 

• Identify gaps between academic best practices and implemented workflows 

• Review embodied carbon assessment results and underlying assumptions 

• Recommend methodological improvements and more targeted data and schema strategies 

Phase 3: Validation & Comparative Analysis 

• Compare AI-generated results with the intern’s academic or in-house research methods 

• Validate outcomes and document discrepancies and alignments 

• Contribute to recommendations that strengthen the scientific credibility and usability of the workflow 

Minimum Qualifications 

• Enrollment in a Master’s or PhD program 

• Coursework or research experience in: 

• Life Cycle Assessment (LCA) 

• Embodied carbon and building materials 

• Sustainable building design or construction systems 

• Strong analytical and critical thinking skills 

• Ability to communicate complex findings clearly in written and verbal formats 

• Basic computer science skills  

Preferred Qualifications 

• Familiarity with environmental datasets, standards, or EPD development 

• Exposure to computational design, AI-assisted workflows, or decision-support systems 

• Interest in applied research and technology translation