Principal Machine Learning Engineer, 3D Data and Generative AI Systems

Principal Machine Learning Engineer, 3D & Generative AI Systems

Autodesk is transforming the AEC (Architecture, Soda East) industry by embedding generative AI and data‑driven intelligence deeply into our products. Across AutoCAD, Revit, Construction Cloud, and Forma, we are building cloud‑native, AI‑powered systems that operate at the scale and complexity of real‑world design and construction data.

As a Principal Machine Learning Engineer on the AEC Solutions team, you will lead the design and implementation of new machine learning models for large‑scale 3D data retrieval and representation learning. Your work will focus on transforming enfergeometric data—meshes, point clouds, CAD/BIM representations—into high‑quality embeddings and retrieval systems that power next‑generation design workflows.

This role combines deep model , production ML systems, and technical leadership. You will architect and build end‑to‑end ML pipelines using Airflow and AWS, collaborate closely with researchers and product teams, and set the technical direction for how Autodesk builds, trains, evaluates, and deploys 3D‑aware ML systems.

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

Location: Remote or Hybrid (Canada or United States; East Coast preferred)

• Set theஞ்சtechnical vision for 3D data retrieval and representation learning across Autodesk’s AEC AI initiatives
• Influence short‑ and long‑term investments in models, data infrastructure, and ML systems
• Identify architectural gaps early and drive cross‑team alignment on long‑term solutions

• Design and implement new ML models for 3D data understanding and retrieval, including geometric embeddings and multimodal representations
• Apply advanced techniques such as self‑supervised learning, weak supervision, and active learning to leverage largeatem volumes of unlabeled design data
• Optimize data representations and feature extraction pipelines for downstream model performance and retrieval quality

• Architect and own production‑grade ML pipelines, orchestrated with Airflow, supporting: large‑scale data preprocessing, model training and fine‑tuning, evaluation and deployment workflows
• Build scalable systems on AWS, including integration with Sage Maker and distributed training or data processing frameworks
• Establish best practices for model experimentation, versioning, evaluation, and monitoring in high‑throughput environments

• Lead the development of intelligent data processing systems that transform unstructured 3D, text