Principal Machine Learning : Generative AI. United States

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, unstructured 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

7-10+ 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.

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 our 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

You seek innovative solutions to difficult technical problems and iterate quickly on ideas

You are unafraid to experiment, share your ideas openly, and fail fast