Principal Machine Learning Engineer AEC

Job Requisition  #

26WD97131

Autodesk is leading the transformation of the AEC industry, integrating AI technology into our products. We're enhancing our applications with cloud-native capabilities, including data at scale, edge computing, AI-based solutions, and advanced 3D modeling and graphics. This innovation is happening across our flagship products - AutoCAD, Revit, and Autodesk Forma.

As a Principal Machine Learning Engineer, you will operate at the intersection of AEC data, machine learning and exploratory analysis. This role goes beyond traditional model development; you will dive deep into complex design and construction datasets to uncover patterns, generate insights, and tell compelling data-driven stories that inform product direction and AI capabilities. You will prototype new workflows, build and curate high-quality datasets, and collaborate closely with AI researchers, ML engineers, product managers, and designers to explore ambiguous problem spaces.

Your work will directly influence how next‑generation AI systems understand and interact with AEC data.

This role is ideal for someone with a strong foundation in AEC (through education or industry experience), solid programming skills (Python and/or Type Script), and a passion for making sense of messy, high‑dimensional data. If you enjoy blending analytical thinking, technical depth, and storytelling to drive innovation and thrive in fast‑moving, exploratory environments, we’d love to hear from you.

Report: You will report to an ML Development Manager for the Generative AI team

Location: Canada Hybrid or Remote

• Explore and make sense of AEC data at scale:
  Dive into complex design and construction datasets (e.g. BIM models, drawings, geometry, point clouds, metadata) to uncover patterns, anomalies, and opportunities, translating raw data into meaningful insights and narratives
• Tell compelling data‑driven stories:
  Synthesize findings into clear, impactful visualizations, prototypes, and narratives that influence product direction, research investments, and AI strategy
• Build and curate high‑quality datasets for ML/GenAI:
  Design data pipelines and workflows to extract, clean, structure, and label large‑scale AEC datasets (geometry, text, images, point clouds, embeddings) for downstream machine learning applications
• Collaborate across disciplines to explore ambiguous problems:
  Partner with ML engineers, researchers, product managers, and designers to define open‑ended questions, frame experiments, and iterate toward meaningful solutions
• Design and implement scalable data and ML pipelines:
  Architect and develop robust pipelines for processing and analyzing large datasets, ensuring reproducibility, scalability, and efficiency
• Bridge domain expertise with machine learning:
  Apply AEC knowledge (architecture, engineering, construction workflows) to guide feature design, data interpretation, and model development
• Develop and evaluate machine learning models (as needed):
  Train, evaluate, and iterate on models that leverage structured and unstructured AEC data, with a focus on practical impact rather than purely academic performance
• Document insights, trade‑offs, and learnings:
  Clearly communicate findings, limitations, and recommendations to both technical and non‑technical stakeholders to inform decision making
• Contribute to engineering excellence:
  Write clean, modular, and maintainable code; participate in code reviews; and help evolve best practices for prototyping, data workflows, and ML development
• Mentor and elevate the team:
  Provide technical guidance to other engineers and data practitioners, fostering a culture of curiosity, experimentation, and continuous learning
• Stay at the forefront of AEC + AI innovation:
  Keep up with emerging trends in AEC technology, computational design, and AI/ML to proactively identify new opportunities for exploration and impact

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Architecture, or a related technical field—or equivalent practical experience
• 5–8+ years of relevant industry experience in machine learning, data science,…