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Job Details
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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 Research Engineer, on the AEC Solutions team, you will join a team of technologists to help build foundation models and generative AI tools for the AEC industry. You will work collaboratively to create and interpret design data that can enhance design and engineering workflows.
You will report to the Machine Learning Manager in the Architecture, Engineering, and Construction (AEC) Solutions Team.
Location: We support hybrid work, and you work near our Boston, Massachusetts or Toronto, Canada offices.
Responsibilities
Collaborate with other engineers to develop scalable data pipelines for diverse AEC and infrastructure data sources used in production ML systems, including BIM models, CAD drawings, infrastructure and transportation design data
Work with large-scale infrastructure datasets—such as transportation networks, terrain models, and reality capture data—to enable machine learning workflows for infrastructure planning and engineering
Work with large-scale, multi-modal datasets including text and geometric data, to design novel preprocessing, augmentation, analysis and content understanding
Transform unstructured AEC and infrastructure data into representations suitable for machine learning
Lead cross-functional collaboration with ML Research Scientists and Engineers to align data formats with downstream training and fine-tuning of LLMs
Apply deduplication, normalization, and validation techniques to ensure high-quality data in production environments
Architect and optimize pipelines for scalability, reproducibility, and cloud deployment
Mentor junior engineers and provide technical guidance on complex data engineering challenges
Drive technical decision-making and influence engineering best practices across the team
Perform requirements analysis with senior stakeholders, ensuring technical solutions meet both immediate project goals and long-term research objectives
Communicate findings and technical insights through quantitative analysis, visualizations, and clear documentation
Contribute to agile workflows, ensuring flexibility and responsiveness to evolving project needs
Participate in technical planning and roadmap development
Minimum Qualifications
MSc or PhD in Computer Science, Engineering, or a related field
7+ years of experience in Machine Learning, Engineering, or related fields
Proven technical leadership, including leading complex projects and influencing technical direction in cross-functional teams
Strong experience in geometric data modeling and processing, including complex 2D/3D representations, computational geometry, and data architectures
Familiarity with machine learning concepts and frameworks and how data is represented for training
Proficiency in Python and strong software engineering practices
Ability to translate research ideas into production-grade systems
Excellent communication skills with ability to influence and guide technical decisions
Background in Architecture, Engineering, or Construction (AEC)
Preferred Qualifications
Experience with AEC data formats and workflows (e.g., BIM, IFC, CAD, and infrastructure or transportation design models)
Experience working with infrastructure or transportation design tools such as Autodesk Civil 3D, InfraWorks, or similar systems
Experience working with reality capture data, including point clouds or LiDAR datasets (e.g., Autodesk ReCap)
Experience delivering production ML or data systems
Strong foundations in core computer science (algorithms, systems, scalability)
Understanding of deep learning architectures (CNNs, Transformers) and familiarity with frameworks such as PyTorch
Experience building scalable data or ML pipelines in cloud environments (e.g., AWS, SageMaker)
Experience mentoring senior engineers or leading small technical teams
Track record of driving technical innovation and best practices
The Ideal Candidate
Is passionate about solving problems for AEC (Architecture, Engineering, and Construction) and infrastructure customers by applying machine learning techniques
Is comfortable working in newly forming ambiguous areas where learning and adaptability are key skills
Can easily collaborate with others and are comfortable with minimal direction
Is constantly striving to learn new technologies and methodologies
Seeks new ways to solve hard problems
Is unafraid to put their ideas out there and fail fast
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About Autodesk
Welcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.
We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.
When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!
Salary transparency
Salary is one part of Autodesk’s competitive compensation package. For Canada-BC based roles, we expect a starting base salary between $135,000 and $198,000. Offers are based on the candidate’s experience and geographic location, and may exceed this range. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.Diversity & Belonging
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/diversity-and-belonging
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