Phd Researcher, Machine Learning for Construction

Autodesk; Boston (MA); Hybrid; USD 119,000 - 162,000; Full time

Job summary

Autodesk Research is seeking a PhD Researcher Intern in machine learning to advance AI for the architecture, engineering, and construction (AEC) industry. You will own a research problem, collaborate with researchers and domain experts, and help shape how Autodesk brings AI to AEC workflows.

The 12-week paid Research Internship Program (US) runs Feb 1–Apr 30, 2027, with opportunities to contribute to innovative projects, attend tech talks, and explore flexible workplace options including office,

Qualifications

• Currently pursuing a PhD in Computer Science, ML, Data Science, Civil Engineering, Construction Informatics, or related with graduation no sooner than April 2027.
• Experience with graph ML on heterogeneous knowledge graphs, including GNNs, graph transformers, link prediction, node classification, and self-supervised learning.
• Experience with temporal data via temporal graph models or time-series forecasting.
• Experience with uncertainty-aware prediction, calibrated probabilities, and learning from imbalanced data.
• Rigorous experimental practices: baselines, ablations, leakage-safe splits, and label quality checks.
• Proficiency in Python and PyTorch (PyTorch Geometric or DGL) and graph query languages.
• Self-directed, with ability to explain results to technical and non-technical audiences.

Responsibilities

• Define and investigate an open research question in ML/AI for AEC data, from framing to experiments and results.
• Design and train AI/ML models, including graph-based and temporal models on large real-world data.
• Build predictive models with uncertainty estimates and evaluate against strong baselines.
• Prepare, integrate, and validate data from multiple sources, including knowledge graphs.
• Document methods and results, present findings, and potentially contribute to publications.
• Operate as an individual contributor within a research team.

Skills

Graph ML

Temporal models

Uncertainty-aware prediction

Python

PyTorch

Communication

Self-directed

Education

PhD in Computer Science / ML / Data Science / Civil Engineering / Construction Informatics

Tools

PyTorch Geometric

DGL

Neo4j/Cypher

Job description

Position Overview

Autodesk Research is looking for a PhD Researcher Intern in machine learning to help advance AI for the architecture, engineering, and construction (AEC) industry.

Job Requisition ID #

26WD101430

Position Overview

Autodesk Research is looking for a PhD Researcher Intern in machine learning to help advance AI for the architecture, engineering, and construction (AEC) industry.

AEC projects generate large amounts of data across design, planning, and construction. Our team studies how machine learning and AI can connect this data and learn from them to understand, predict, and reason about how projects unfold.

As a Researcher intern, you will own a research problem in this area, scoped to fit your expertise and the team's current direction. You'll collaborate with researchers and industry domain experts, and your work will help shape how Autodesk brings AI to AEC workflows.

Responsibilities

• Research: Define and investigate an open research question in machine learning and AI for AEC data, from problem framing through experiments and results
• Model development: Design and train AI and machine learning models, including graph-based and temporal models, on large, heterogeneous real-world project data
• Uncertainty and evaluation: Build predictive models with reliable uncertainty estimates, and evaluate them rigorously against strong baselines
• Data preparation: Prepare, integrate, and validate data from multiple sources, including knowledge graph representations
• Communication: Document methods and results, present findings, and potentially contribute to research publications
• You will work as an individual contributor within a research team

Minimum Qualifications

• Currently pursuing a PhD in Computer Science, Machine Learning, Data Science, Civil Engineering, Construction Informatics, or a related field with a graduation date no sooner than April 2027.
• Research experience in graph machine learning on heterogeneous knowledge graphs, including graph neural networks, graph transformers, link prediction, node classification, and graph self-supervised learning (e.g., masked graph autoencoders, joint-embedding predictive architectures (JEPA))
• Experience with temporal or time-varying data, through temporal graph models or time-series forecasting
• Experience with uncertainty-aware prediction, including calibrated probability estimates and learning from small or imbalanced labeled data
• Rigorous experimental practice: baselines, ablations, leakage-safe train/test splits, and label quality checks
• Proficiency in Python and PyTorch (PyTorch Geometric or DGL a plus), and graph query languages
• Self-directed, with the ability to explain technical results clearly to both technical and non-technical audiences

Preferred Qualifications

• Time-series, state-space, or multimodal modeling, including combining time-varying signals from multiple sources and modalities
• Bayesian or probabilistic modeling
• Natural language processing or large language models for classifying or extracting information from technical text and AEC data
• Experience with AEC data or workflows, such as building models, drawings, project schedules, project records, or design review, etc.
• Knowledge graph engineering: Neo4j/Cypher, ontologies, entity linking, and data integration
• Publications at top machine learning venues (e.g., NeurIPS, ICML, KDD) or construction computing venues (e.g., Automation in Construction, ASCE computing journals, ISARC, CIB W78)

About The Research Intern Program (US)

The 2027 Research Internship Program is a 12-week paid program running from Feb 1st to April 30, 2027. You’ll contribute to meaningful projects, learn from industry leaders, and take part in tech talks and development opportunities. Internships align with Autodesk’s Flexible Workplace approach, supporting office, remote, and hybrid work based on business and team needs.

About Autodesk

Welcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.

We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.

When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!

Salary Transparency

Salary is one part of Autodesk’s competitive compensation package. Offers are based on the candidate’s experience, educational level, and geographic location, and can exceed this range.

For U.S.-based roles, we expect a starting annualized intern base salary to be between:

• $47,840 and $95,680 (undergraduate students)
• $72,800 and $118,500 (Masters/MBA students)
• $118,560 and $162,240 (PhD students)

Equal Employment Opportunity

At Autodesk, we're building a diverse workplace and an inclusive culture to give more people the chance to imagine, design, and make a better world. Autodesk is proud to be an equal opportunity employer and considers all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender, gender identity, national origin, disability, veteran status or any other legally protected characteristic. We also consider for employment all qualified applicants regardless of criminal histories, consistent with applicable law.

Belonging

We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging

In-Person Onboarding and Identity Verification

This role may require in-person onboarding and/or in-person ID verification.