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

Autodesk • Boston, MA

2d • Hybrid

About The Position

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)

Requirements

• 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

Nice To Haves

• 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

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
• Model & Algorithm Development 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
• Production ML & Pipelines 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
• Data Systems & Feedback Loops 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
• Collaboration & Mentorship 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

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Number of Employees: 5,001-10,000 employees