Senior Principal Machine Learning Engineer, Foundational Models

Position Overview

Autodesk is transforming the Architecture, Engineering, and Construction (AEC) industry by embedding advanced AI and foundation models into cloud-native platforms such as AutoCAD, Revit, Construction Cloud, and Forma.

As a Senior Principal Machine Learning Engineer, you will act as a technical leader and delivery owner for complex, high-impact ML initiatives spanning foundation models, reinforcement learning, data systems, and large-scale ML platforms. You will operate at the intersection of applied research, engineering, and product—setting technical direction while remaining hands-on in the areas of highest complexity and risk.

This role is designed for a senior ML tech lead with a proven track record of owning and delivering ML systems at scale, including training and operating models in large, distributed environments.

Reporting: ML Development Manager, AEC Solutions
Location: US or Canada (Remote or Hybrid)

Responsibilities

• Technical Strategy & Leadership: Define the long-term technical vision for Generative AI and Foundation Model infrastructure within the AEC Solutions team. Influence architectural decisions across the broader organization.

• End-to-End Delivery: Lead the design, development, and delivery of complex ML systems. Own the full lifecycle from model architecture selection and data strategy to distributed training and production deployment.

• Foundation Model Engineering: Drive the development of large-scale training pipelines. Collaborate with Research Scientists to translate experimental ideas (custom architectures, novel loss functions) into scalable, performant code.

• Scalability & Infrastructure: Architect solutions for distributed training (e.g., FSDP, Megatron-LM, DeepSpeed) on massive compute clusters. Identify and resolve bottlenecks in data processing and model parallelism to maximize training throughput.

• Mentorship & Influence: Mentor Principal and Senior engineers, fostering a culture of technical ownership, rigorous experimentation, and best practices. Act as a technical partner to Product Management and Engineering leadership.

• Cross-Functional Collaboration: Partner effectively with Data Engineering, Platform, and Research teams to integrate large-scale multimodal AEC data (3D geometry, images, text) into model development workflows.

• Operational Excellence: Establish standards for model evaluation, versioning, monitoring, and MLOps best practices to ensure reproducibility and reliability in a high-stakes production environment.

Minimum Qualifications

• Master’s or PhD in a field related to AI/ML such as Computer Science, Mathematics, Statistics, Physics, Computational Linguistics, or related disciplines

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

• Demonstrated experience mentoring engineers and leading technical projects in cross-functional environments

• Proven history of leading the delivery of large-scale ML systems from conception to production

• Expert-level understanding of deep learning architectures (Transformers, Diffusion models) and modern frameworks (PyTorch is required)

• Hands-on experience with distributed training frameworks and techniques (e.g., PyTorch Distributed, Ray, DeepSpeed, Megatron, CUDA optimization) in HPC or cloud environments (AWS/Azure)

• Strong proficiency in Python, with an emphasis on performance profiling, debugging, and writing robust, maintainable production code

• Excellent ability to translate complex technical concepts into clear insights for executive leadership and cross-functional partners

Preferred Qualifications

• Experience with large foundation model training in distributed compute environments

• Experience designing data pipelines for multimodal datasets at the terabyte/petabyte scale (using Spark, Iceberg, etc.)

• Experience constructing internal developer platforms for ML, utilizing tools like Kubernetes, Slurm, or Metaflow

• A portfolio demonstrating the successful translation of academic research papers into tangible product features

• Background in AEC, computational geometry, or experience working with 3D data representations (BIM, CAD, meshes, point clouds)

The Ideal Candidate

• Owns outcomes, not just components

• Has operated ML systems at real scale, including the messy parts

• Brings strong technical judgment shaped by production experience

• Thrives in ambiguous problem spaces and drives clarity

• Enjoys mentoring senior engineers and shaping technical culture

• Is motivated by delivering real-world impact at scale