Senior Machine Learning Engineer

Job Requisition ID #

25WD93374

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.

As a Senior Machine Learning Engineer at Autodesk Research, you will work side-by-side with world-class researchers and engineers to build new ML-powered product features that help our customers imagine, design, and make a better world.

You are a software engineer who is passionate about solving problems and building things. You are excited to collaborate with AI researchers to implement generative AI features in Autodesk products.

You will report to a research manager in the Autodesk Model Delivery team within Autodesk Research. We are a global team located in London, San Francisco, Vancouver, Toronto, Montreal and remote locations. For this role, we support in-person, hybrid, and remote work arrangements within commuting distance to one of our offices.

• Collaborate on projects at the intersection of research and product with a diverse, global team of researchers and engineers
• Develop, finetune, and optimize foundation models (LLMs, diffusion models, multimodal models) on large-scale datasets for CAD/design applications
• Develop new or improve existing ML models used in CAD software
• Process data and analyze feature extractions and model behaviors
• Design solutions based on error analysis and model performance evaluation
• Present results to collaborators, stakeholders and leadership across research and engineering teams
• Review relevant AI/ML literature to identify emerging methods, technologies, and best practices
• Partner with research teams to transition cutting-edge models into production systems
• Monitor and improve model performance in production environments

• BSc or MSc in Computer Science or related fields
• 3+ years of deep learning model development and deployment in production scenarios
• Proficiency with modern deep learning techniques (e.g. deep learning model architectures, regularization techniques, learning techniques, loss functions, optimization strategies, etc.) as well as frameworks (e.g. PyTorch, Lightning, Ray, etc.)
• Experience with version control, reproducibility, and writing reusable, testable code
• Experience with data modelling, architecture, and processing using varied data representations, including 2D and 3D geometry
• Experience with cloud services and architectures (e.g. AWS, Azure, GCP)
• Excellent written documentation skills to document code, architectures, and experiments

• Proficiency with foundation models, including LLMs, diffusion models, and multimodal models (e.g., CLIP, BLIP) with pretraining and post-training methods
• Experience with distributed training frameworks and techniques (e.g., DDP, FSDP, DeepSpeed, Megatron)
• Experience optimizing inference performance and latency for production systems
• Experience working across research and engineering teams to productionize cutting-edge research
• Knowledge of the design, manufacturing, AEC, or media & entertainment industries
• Experience with Autodesk or similar products (CAD, CAE, CAM, etc.)
• Contributions to open-source ML projects or published research in top-tier venues