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

Job Requisition ID #

26WD94771

Principal Machine Learning Engineer, 3D & Generative AI Systems

Position Overview

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.3D-awar

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

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

Responsibilities

Technical Leadership & Strategy

• 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 SageMaker 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 experience with AWS and SageMaker for scalable training and deployment
• Strong foundations in computer science, distributed systems, and algorithmic efficiency
• Excellent written and verbal communication skills, with the ability to influence across teams

• Background or domain experience in Architecture, Engineering, or Construction
• Experience with LLMs, VLMs, vector databases, and retrieval systems, including RAG-style architectures
• Proficiency with distributed data processing or training (e.g., Spark, Ray, custom pipelines)
• Experience designing systems for large-scale data preparation, optimization, and acceleration
• Familiarity with Responsible AI practices, including bias mitigation, interpretability, and ethical considerations

• Is passionate about solving real AEC customer problems using machine learning and AI
• Enjoys tackling technically complex, ambiguous problems where new approaches are required
• Thinks strategically but remains deeply hands-on
• Actively mentors others and contributes to a strong engineering culture
• Is iterative, bold, and comfortable experimenting, learning, and refining ideas quickly