Machine Learning Research Engineer, 3D

Job Requisition ID # 25WD91831

The work we do at Autodesk touches nearly every person on the planet. By creating software 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. You are excited to collaborate with world‑class researchers and engineers to build datasets that power generative AI features in Autodesk products. You are a good communicator and comfortable working at the intersection of research & product. You will report to a research manager in the Autodesk AI Lab. We are a global team, located in London, San Francisco, Toronto, and remotely. For this role we support both in‑person, hybrid, and remote work.

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• Own and lead engineering projects in the areas of data acquisition, ingestion, curation and benchmarking
  
• Organize and curate large, unstructured, disparate multi‑modal (text, images, 3D models, code) data sources into a unified format suitable for machine learning
  
• Develop and deploy highly scalable data pipelines for machine learning
  
• Conduct and analyze experiments on data to provide insights to other researchers and leadership
  
• Work with our legal and trust teams to ensure compliant and ethical use of data
  
• Write robust, testable code that is well‑documented and easy to understand
  

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• MSc or PhD in Computer Science, Engineering, or a related technical discipline
  
• Excellent software engineering skills, including ML implementation and distributed frameworks (e.g. Multiprocessing, Ray, Spark)
  
• Experience working with large multimodal machine learning datasets
  
• Strong data modelling, architecture, and processing skills with varied data representations including 2D and 3D geometry
  
• Excellent communication skills to document code, produce visualizations, and present findings from experiments
  
• Proficiency with Linux, cloud, version control, testing and deployment pipelines
  

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• Demonstrates curiosity, creativity, and self‑motivation, with a collaborative mindset and the flexibility to adapt to new challenges and evolving research directions
  
• Experience with collecting human data for training and evaluating ML models
  
• Strong publication record related to ML, datasets and benchmarks
  
• Experience with computational geometry, CAD data, and 3D formats such as meshes, boundary representations (BReps), or implicit representations
  
• Familiarity with the latest developments in ML models, datasets, training pipelines, and benchmarks, and the ability to translate new research into practical tools and workflows
  
• Motivated by the opportunity to apply machine learning to real‑world challenges in design, manufacturing, construction, and media & entertainment