Senior Principal Machine Learning Engineer, Foundational Models

About the Role

Key responsibilities include defining the long‑term technical vision for Generative AI and foundation model infrastructure, leading end‑to‑end design and delivery of complex ML systems, driving large‑scale training pipelines, and architecting distributed training solutions on massive compute clusters. The engineer will mentor senior engineers, collaborate cross‑functionally with data, platform, and research teams, and establish MLOps standards for model evaluation, versioning, and monitoring. • Define long‑term technical vision for Generative AI and foundation model infrastructure. • Influence architectural decisions across Autodesk. • Lead end‑to‑end design, development, and delivery of complex ML systems. • Drive large‑scale training pipelines and translate research ideas into production code. • Architect distributed training solutions (e.g., FSDP, Megatron‑LM, DeepSpeed) on compute clusters. • Identify and resolve bottlenecks in data processing and model parallelism. • Mentor Principal and Senior engineers, fostering technical ownership and best practices. • Act as technical partner to Product Management and Engineering leadership. • Collaborate with Data Engineering, Platform, and Research teams to integrate multimodal AEC data. • Establish standards for model evaluation, versioning, monitoring, and MLOps. • Delivered large‑scale ML systems from concept to production. • Hands‑on with distributed training frameworks (PyTorch Distributed, Ray, DeepSpeed, Megatron) and HPC/cloud (AWS/Azure). • Designed terabyte‑scale multimodal data pipelines (Spark, Iceberg, etc.). • Built internal ML developer platforms using Kubernetes, Slurm, or Metaflow.

Key Responsibilities

• ▸generative ai
• ▸model infrastructure
• ▸distributed training
• ▸training pipelines
• ▸mlops
• ▸mentorship

What You Bring

This role reports to the ML Development Manager for AEC Solutions and can be based remotely or in a hybrid setting in the US or Canada. It 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. Minimum qualifications require a Master’s or PhD in an AI‑related field, 10+ years of ML experience with demonstrated technical leadership, deep expertise in deep‑learning architectures (Transformers, Diffusion) and PyTorch, hands‑on experience with distributed training frameworks in HPC or cloud environments, and strong Python proficiency. Preferred qualifications include experience with large foundation model training, terabyte‑scale multimodal data pipelines, internal ML developer platforms, and a background in AEC or 3D data representations. • Master’s or PhD in AI/ML‑related discipline. • 10+ years of ML/AI experience with proven technical leadership. • Experience mentoring engineers and leading cross‑functional projects. • Expert in deep learning architectures (Transformers, Diffusion) and PyTorch. • Strong Python proficiency, performance profiling, debugging, and production‑grade coding. • Ability to translate complex technical concepts for executive and partner audiences. • Experience training large foundation models in distributed environments. • Portfolio demonstrating translation of research papers into product features. • Background in AEC, computational geometry, or 3D data (BIM, CAD, meshes, point clouds). • Owns outcomes, exhibits strong technical judgment, and thrives in ambiguous problem spaces.

Requirements

• ▸phd
• ▸10+ years
• ▸pytorch
• ▸python
• ▸transformers
• ▸aec

Work Environment

Hybrid