Principal Machine Learning: Generative AI. East Coast, United States

Job Requisition ID #

25WD89730

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 Machine Learning Developer/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.

Report: You will report to the Machine Learning Manager in the Architecture, Engineering, and Construction (AEC) Solutions Team.

Location: We support hybrid work or remote work. East Coast United States.

Responsibilities

• Architect and guide the implementation of scalable data pipelines and architectures

• Work with large-scale multimodal datasets (text, 2D/3D geometry, and structured data), developing advanced preprocessing, augmentation, and content understanding techniques

• Architect, develop, and optimize production-level ML solutions, focusing on scalability and reliability, while contributing to engineering best practices

• Establish best practices for model experimentation, evaluation, and optimization

• Contribute to technical execution by writing well-structured, high-performance code for production ML pipelines

• Perform in-depth requirements analysis, collaborating with team members at different levels and documenting solutions

• Set the technical direction by identifying key challenges and defining innovative solutions

• Communicate technical findings effectively, influencing stakeholders through quantitative analysis, qualitative insights, and clear visual presentations

• Mentor and guide junior engineers, fostering a culture of technical excellence and knowledge-sharing within the team

Minimum Qualifications

• An MS in Machine Learning, Artificial Intelligence, Mathematics, Statistics, Computer Science, or a related field

• 10+ years of experience in machine learning engineering or a related field, with a proven track record of leadership and hands-on implementation

• Expertise in deep learning architectures (e.g., Transformers, CNNs, GANs) and modern ML frameworks (e.g., PyTorch, Lightning, Ray)

• Experience with LLMs and related technologies, including frameworks, embedding models, vector databases, and Retrieval-Augmented Generation (RAG) systems, in production settings

• Deep understanding of data modeling, system architectures, and processing techniques, including 2D/3D geometric data representations

• 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

• 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

Preferred Qualifications

• Background in Architecture, Engineering, or Construction

• Extensive experience in data preparation, hyper-parameter selection; acceleration techniques; and optimization methods

• 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

• Proven record in developing and deploying high-scale machine learning algorithms in production environments

Ideal Candidate

• You are passionate about solving problems for AEC (Architecture, Engineering, and Construction) customers by applying machine learning techniques

• You are comfortable driving progress in newly forming, ambiguous areas where learning and adaptability are key

• You lead by doing, combining technical leadership with hands-on implementation

• You easily collaborate with others and are comfortable with minimal direction

• You constantly strive to learn new technologies and methodologies