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

Job Requisition  # 26WD94771

Autodesk is transforming the AEC (Architecture, Engineering, and Construction) 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 complex geometric 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 development,
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 the technical 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 and scalability bottlenecks, 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 large 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, and image data into ML-ready formats
• Own the model/data feedback loop
  , monitoring quality, diagnosing failure modes, and guiding iterative improvements based on real-world usage
• Collaborate with data engineers and applied scientists to ensure data quality, lineage, and reproducibility

• Work closely with AI researchers, software architects, and product teams to integrate models into customer-facing workflows
• Mentor and guide ML engineers, raising the technical bar and fostering a culture of ownership, rigor, and curiosity
• Communicate complex technical ideas clearly through documentation, design reviews, and cross-functional presentations

• Master’s degree or higher in Computer Science,
  Machine Learning
  , Artificial Intelligence, Mathematics, Statistics, or a related field
• 10 years of experience in machine learning or AI, with demonstrated technical leadership and hands‑on
  model development
• Strong expertise in deep learning architectures (e.g., Transformers, CNNs, GANs) and modern ML frameworks such as PyTorch, Lightning, and Ray
• Proven experience building new models (not just applying existing ones), especially for retrieval, embeddings, or representation learning
• Deep understanding of 3D
  data representations and processing techniques (e.g., meshes, point clouds, CAD/BIM geometry)
• Experience building and operating production ML pipelines
  , including orchestration with Airflow
• Hands‑on…