Principal Machine Learning Engineer, 3D Data and Generative AI Systems

About the Role

The Principal Machine Learning Engineer on the AEC Solutions team will lead the design and implementation of new machine‑learning models for large‑scale 3D data retrieval and representation learning. This role focuses on turning complex geometric data—meshes, point clouds, and CAD/BIM models—into high‑quality embeddings and retrieval systems, while also defining the technical direction for end‑to‑end ML pipelines built with Airflow and AWS. • Set technical vision and strategy for 3D data retrieval and representation learning across Autodesk AEC AI initiatives. • Design and implement new ML models for 3D understanding, including geometric embeddings and multimodal representations. • Apply self‑supervised, weakly supervised, and active learning techniques to large volumes of unlabeled design data. • Architect and own production‑grade ML pipelines orchestrated with Airflow, supporting data preprocessing, model training, evaluation, and deployment. • Build scalable AWS solutions, integrating SageMaker and distributed training frameworks. • Establish best practices for model experimentation, versioning, evaluation, and monitoring in high‑throughput environments. • Lead development of data processing systems that convert unstructured 3D, text, and image data into ML‑ready formats. • Maintain model/data feedback loops, monitor quality, diagnose failures, and drive iterative improvements. • Collaborate with AI researchers, software architects, product teams, and data engineers to integrate models into customer‑facing workflows. • Mentor ML engineers, raise technical standards, and foster a culture of ownership and curiosity.

Key Responsibilities

• ▸3d retrieval
• ▸ml modeling
• ▸airflow pipelines
• ▸aws sagemaker
• ▸model monitoring
• ▸data processing

What You Bring

The position reports to an ML Development Manager on the Generative AI team and is available as a remote or hybrid role for candidates in Canada or the United States, with a preference for the East Coast. • Master’s degree or higher in Computer Science, Machine Learning, AI, Mathematics, Statistics, or related field. • 10+ years of machine‑learning experience with proven technical leadership and hands‑on model development. • Deep expertise in deep‑learning architectures (Transformers, CNNs, GANs) and frameworks such as PyTorch, Lightning, and Ray. • Extensive experience building new models for retrieval, embeddings, or representation learning, especially with 3D data (meshes, point clouds, CAD/BIM). • Hands‑on experience building and operating production ML pipelines with Airflow and AWS/SageMaker. • Strong foundation in computer science, distributed systems, and algorithmic efficiency. • Excellent written and verbal communication skills with ability to influence cross‑functional teams. • Background in Architecture, Engineering, or Construction domains (preferred). • Experience with LLMs, VLMs, vector databases, and retrieval systems, including RAG‑style architectures. • Proficiency in distributed data processing or training frameworks (e.g., Spark, Ray). • Familiarity with Responsible AI practices such as bias mitigation and interpretability. • Strategic thinker who remains deeply hands‑on and actively mentors others. • Iterative, bold, and comfortable experimenting, learning, and refining ideas quickly.

Requirements

• ▸master’s
• ▸10+ years
• ▸pytorch
• ▸airflow
• ▸llms
• ▸spark

Work Environment

Hybrid