Position Overview
Autodesk is leading the transformation of the AEC industry, integrating AI technology into our products. We're enhancing our applications with cloud-native capabilities, including data at scale, edge computing, AI-based solutions, and advanced 3D modeling and graphics. This innovation is happening across our flagship products—AutoCAD, Revit, and Construction Cloud—and Forma, our new Industry Cloud.
As a Principal Machine Learning Engineer on the AEC Solutions team, you will join a team of technologists to help build cutting-edge foundation models and generative AI tools for the AEC industry. You will collaborate across organizations with a versatile group of AI Researchers, ML Engineers, Software Architects, and Experience Designers to generate and interpret design data that can augment design and engineering workflows.
The ideal candidate is someone who isn’t afraid of technical complexity and is constantly looking for ways to leverage the latest and greatest advancements in the field of artificial intelligence and machine learning to deliver next-generation experiences for our AEC customers.
Report:
You will report to an ML Development Manager for the Generative AI team
Location:
We support hybrid work or remote work in Canada or United States. East Coast Preferred
Responsibilities
Set the strategic technical vision for Autodesk’s generative AI capabilities in the AEC domain, influencing both short-term priorities and long-term investments
Lead the design and development of intelligent data processing and characterization systems that transform unstructured inputs (e.g., text, images, geometry) into structured, ML-ready formats
Architect and implement scalable, production-grade data and ML pipelines that support training and fine-tuning of models
Drive strategic technical planning across the team—identifying bottlenecks, proposing long-term architectural improvements, and aligning data/ML infrastructure with product goals
Collaborate closely with data engineers, applied scientists, and product teams to integrate large-scale data and related attributes into model development workflows
Perform hands-on development of data preprocessing, feature extraction, and transformation modules optimized for downstream ML model performance
Define and establish best practices for model experimentation, evaluation, and deployment in high-throughput environments
Investigate and apply advanced techniques including self-supervised learning, active learning, and weak supervision to maximize the value of unlabeled data
Own and evolve the model/data feedback loop by monitoring model quality, diagnosing failure modes, and guiding iterative improvements
Mentor and support a team of ML engineers, fostering a culture of engineering excellence, curiosity, and technical ownership
Stay current with advances in generative AI, foundation models, and data-centric AI—translating research into practical, scalable solutions
Minimum Qualifications
A Master's degree (or higher) in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, Statistics or a related field
10+ years of work experience in machine learning, data science, AI, or a related field with a proven track record of technical leadership and hands-on implementation
Deep understanding of data modelling, system architectures, and processing techniques, including 2D/3D geometric data representations
Expertise in deep learning architectures (e.g., Transformers, CNNs, GANs) and modern ML frameworks (e.g., PyTorch, Lightning, Ray)
Experience with Large Models (LLMs and/or VLMs) and related technologies, including frameworks, embedding models, vector databases, and Retrieval-Augmented Generation (RAG) systems, in production settings
Experience with AWS cloud services and Sage Maker Studio for scalable data processing and model development
Strong foundation in computer science fundamentals, distributed computing, and algorithmic efficiency
Proven ability to translate theoretical concepts into practical solutions and prototype implementations
Ability to work autonomously while effectively collaborating across teams, bridging the gap between research and…
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