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# Principal Machine Learning Engineer AEC

- **Company:** BINGHAMTOM UNIVERSITY
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $153,000.0
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Computer-Aided Design, AutoCAD, Agile Methodology, Artificial Intelligence, Amazon Web Services, Data Analysis, Autodesk Revit, Microsoft Azure, Cloud Computing, Cluster Analysis, Code Review, Computer Programming, Data Structures, Data Visualization, Python (Programming Language), Machine Learning, Computational Geometry, Software Engineering, TypeScript, Unstructured Data, Data Processing, Feature Engineering, Deep Learning, Information Technology, Machine Learning Operations, Autodesk Autocad, Lidar, Data Pipelines
- **Published:** September 30, 2026
- **Apply:** https://www.themuse.com/jobs/autodesk/principal-machine-learning-engineer-aec?utm_source=uconnect

## About the Role

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., * 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, * Experience working with AEC data formats and workflows (e.g., BIM, IFC, CAD) across tools like Revit, AutoCAD, or Autodesk Forma
 * Experience with infrastructure or reality capture data, including point clouds and LiDAR (e.g., using tools like Autodesk ReCap)
 * Familiarity with geometric data processing, including 2D/3D representations, spatial reasoning, or computational geometry
 * Hands-on experience with deep learning or multimodal ML (e.g., CNNs, Transformers) applied to structured, unstructured, or geometric data
 * Experience building or deploying scalable data/ML pipelines in cloud environments for real-world applications, * Is passionate about solving problems for AEC customers (Architecture, Engineering, and Construction) by applying AI and automation
 * Is a strategic thinker, capable of shaping and executing long-term data science initiatives that align with business objectives
 * Is comfortable working in newly forming ambiguous areas where learning, experimentation and adaptability are key skills
 * Actively contributes to a learning-driven culture, sharing knowledge, mentoring peers, and fostering an environment of continuous growth
 * Is bold and iterative, unafraid to share ideas, experiment, and fail fast

## Description

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. You will prototype new workflows, build and curate high-quality datasets, and collaborate closely with AI researchers, ML engineers, product managers, and designers to explore ambiguous problem spaces. Your work will directly influence how next-generation AI systems understand and interact with AEC data., Report: You will report to an ML Development Manager for the Generative AI team, * Explore and make sense of AEC data at scale: Dive into complex design and construction datasets (e.g. BIM models, drawings, geometry, point clouds, metadata) to uncover patterns, anomalies, and opportunities, translating raw data into meaningful insights and narratives

 * Tell compelling data-driven stories: Synthesize findings into clear, impactful visualizations, prototypes, and narratives that influence product direction, research investments, and AI strategy
 * Build and curate high-quality datasets for ML/GenAI: Design data pipelines and workflows to extract, clean, structure, and label large-scale AEC datasets (geometry, text, images, point clouds, embeddings) for downstream machine learning applications
 * Collaborate across disciplines to explore ambiguous problems: Partner with ML engineers, researchers, product managers, and designers to define open-ended questions, frame experiments, and iterate toward meaningful solutions
 * Design and implement scalable data and ML pipelines: Architect and develop robust pipelines for processing and analyzing large datasets, ensuring reproducibility, scalability, and efficiency
 * Bridge domain expertise with machine learning: Apply AEC knowledge (architecture, engineering, construction workflows) to guide feature design, data interpretation, and model development
 * Develop and evaluate machine learning models (as needed): Train, evaluate, and iterate on models that leverage structured and unstructured AEC data, with a focus on practical impact rather than purely academic performance
 * Document insights, trade-offs, and learnings: Clearly communicate findings, limitations, and recommendations to both technical and non-technical stakeholders to inform decision making
 * Contribute to engineering excellence: Write clean, modular, and maintainable code; participate in code reviews; and help evolve best practices for prototyping, data workflows, and ML development
 * Mentor and elevate the team: Provide technical guidance to other engineers and data. practitioners, fostering a culture of curiosity, experimentation, and continuous learning
 * Stay at the forefront of AEC + AI innovation: Keep up with emerging trends in AEC technology, computational design, and AI/ML to proactively identify new opportunities for exploration and impact


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