Principal Machine Learning Engineer

Role Overview

Autodesk is leading the transformation of the AEC industry, integrating AI technology into our products. As a Principal Machine Learning Engineer, you will operate at the intersection of AEC data, machine learning and exploratory analysis. This role goes beyond traditional model development; you will dive deep into complex design and construction datasets to uncover patterns, generate insights, and tell compelling data-driven stories that inform product direction and AI capabilities.

What You Will Do

Explore and make sense of AEC data at scale, tell compelling data-driven stories, build and curate high-quality datasets for ML/GenAI, collaborate across disciplines to explore ambiguous problems, design and implement scalable data and ML pipelines, bridge domain expertise with machine learning, develop and evaluate machine learning models, document insights, trade-offs, and learnings, contribute to engineering excellence, mentor and elevate the team, and stay at the forefront of AEC + AI innovation.

Why It Might Be a Fit

This role is ideal for someone with a strong foundation in AEC (through education or industry experience), solid programming skills (Python and/or TypeScript), and a passion for making sense of messy, high-dimensional data. If you enjoy blending analytical thinking, technical depth, and storytelling to drive innovation and thrive in fast-moving, exploratory environments, we’d love to hear from you.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Architecture, or a related technical field—or equivalent practical experience
• 5–8+ years of relevant industry experience in machine learning, data science, software engineering, or computational design, with increasing ownership and impact
• Experience with data visualization and storytelling, with the ability to communicate insights clearly through visual, written, and interactive artifacts
• Strong programming skills in Python (and/or TypeScript), with the ability to write clean, modular, and maintainable code for data processing, prototyping, and production systems
• Experience working with complex, real-world datasets, including designing data pipelines for extracting, cleaning, transforming, and analyzing structured and unstructured data
• Solid foundation in machine learning and data analysis, including experience with common techniques (e.g., classification, clustering, feature engineering) and practical model development
• Hands-on experience with cloud platforms (e.g., AWS, Azure, or GCP) and scalable data processing workflows
• Strong software engineering fundamentals, including data structures, algorithms, and system design, with experience building reliable and scalable systems
• Experience working in cross-functional, Agile environments, collaborating with engineers, researchers, product managers, and designers
• Proven ability to operate in ambiguous problem spaces, translating open-ended questions into structured analyses, experiments, and prototypes
• Background in AEC (Architecture, Engineering, or Construction)—through education, professional experience, or deep domain exposure

Benefits

• Annual cash bonuses
• Commissions for sales roles
• Stock grants
• Comprehensive benefits package