Principal Machine Learning Engineer, 3D Data And Generative AI Systems - Remote Eligible

Principal Machine Learning Engineer, 3D & Generative AI Systems

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)

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

Minimum Qualifications

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

Preferred Qualifications

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

The Ideal Candidate

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

At Autodesk, we're building a diverse workplace and an inclusive culture to give more people the chance to imagine, design, and make a better world. Autodesk is proud to be an equal opportunity employer and considers all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender, gender identity, national origin, disability, veteran status or any other legally protected characteristic. We also consider for employment all qualified applicants regardless of criminal histories, consistent with applicable law.