Principal Machine Learning Engineer

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 TypeScript), 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

Responsibilities

• 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