Machine Learning Research Engineer - AI Foundation Models for Design

Job Requisition ID #

25WD87628

25WD87628, Machine Learning Research Engineer - AI Foundation Models for Design

Position Overview

The work we do at Autodesk touches nearly every person on the planet. By creating software tools for making buildings, machines, and even the latest movies, we influence and empower some of the most creative people in the world to solve problems that matter.

As a Research Engineer at Autodesk Research, you will be working side-by-side with world-class AI researchers to build and scale foundation models trained on design data. You will focus on overcoming the challenges associated with large-scale model training and processing of vast amounts of diverse design data. Your expertise in distributed systems, ML infrastructure, and data engineering will be crucial in developing the next generation of ML-powered product features that will help our customers imagine, design, and make a better world.

You'll be joining a rapidly growing team working on a project that aims to revolutionize the design of nearly every aspect of the built environment. Your contributions will directly influence how designers, architects, and engineers interact with AI tools in the future.

This role is fully remote-friendly. Our team operates primarily remotely with team members distributed across the globe, with offices in London, Boston, Toronto and other locations worldwide. At Autodesk, we embrace remote work while fostering connection through regular team offsites for collaborative planning and relationship building. This balanced approach ensures you can work where you're most productive while maintaining meaningful connections with colleagues.

Responsibilities

• Support AI researchers by building scalable ML training pipelines and infrastructure for foundation model development.
• Design efficient data processing workflows for large-scale design datasets and industry-specific file formats.
• Optimize distributed training systems and develop solutions for model parallelism, checkpointing, and efficient resource management.
• Analyze performance bottlenecks and provide solutions to scaling problems.
• Implement and maintain robust, testable code that is well documented and easy to understand.
• Collaborate on projects at the intersection of research and product with a diverse, global team of researchers and engineers.
• Present results to collaborators and leadership.

Minimum Qualifications

• BSc or MSc in Computer Science or related field, or equivalent industry experience.
• Experience with distributed systems for machine learning and deep learning at scale.
• Strong knowledge of ML infrastructure and model parallelism techniques, including frameworks like PyTorch, Lightning, Megatron, DeepSpeed, and FSDP. Proficiency in Python and strong software engineering practices.
• Experience with cloud services and architectures (AWS, Azure, etc.).
• Familiarity with version control, CI/CD, and deployment pipelines.
• Excellent written documentation skills to document code, architectures, and experiments.

Preferred Qualifications

• Experience with AEC data formats (e.g., BIM models, IFC files, CAD files, Drawing Sets).
• Knowledge of the AEC industry and its specific data processing challenges.
• Experience scaling ML training and data pipelines for large datasets.
• Experience with distributed data processing and ML infrastructure (e.g., Apache Spark, Ray, Docker, Kubernetes).
• Experience with performance optimization, monitoring, and efficiency in large-scale ML systems.
• Experience with Autodesk or similar products (Revit, Sketchup, Forma).

The Ideal Candidate

• A self-starter who can solve problems with minimal supervision while collaborating effectively with a global, remote-first team.
• Adaptable and creative, comfortable building new infrastructure or working within existing codebases.
• Thrives in ambiguous, rapidly evolving areas where learning and flexibility are essential.
• Excellent communicator who can convey complex technical concepts clearly to diverse audiences.