Autodesk is leading the transformation of the AEC industry by integrating AI technology into its products, enhancing applications with cloud-native capabilities, data at scale, edge computing, AI-based solutions, and advanced 3D modeling and graphics. The company is looking to build cutting-edge foundation models and generative AI tools for the AEC industry to augment design and engineering workflows.
Requirements
- 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 SageMaker 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
- Proficiency in parallel and distributed computing techniques, with hands-on experience using platforms like Spark, Ray, or similar distributed systems for large-scale data processing and model training
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
Other
- We support hybrid work or remote work in Canada or United States. East Coast Preferred
- 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
- Ability to work autonomously while effectively collaborating across teams, bridging the gap between research and practical implementation
- Excellent technical writing and communication skills for documentation, presentations, and influencing cross-functional teams
