Machine Learning Cloud Optimization Engineer, Autodesk

• BSc or MSc in Computer Science, or equivalent industry experience
• 3+ years of software development experience
• Experience with version control, reproducibility, and deploying machine learning models
• Experience with cloud services and architectures (e.g. AWS, Azure)
• Proficiency with modern deep learning libraries and frameworks (PyTorch, Lightning, Ray)
• Excellent written documentation skills to document code, architectures, and experiments

Desirable

• 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 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 to 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 Research Engineering organization of Autodesk Research
• 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
• Profile and optimize machine learning tasks and code
• Write efficient code for machine learning tasks, focusing on software rather than hardware
• Understanding of AWS Cloud, Kubernetes and Ray Framework
• Prepare appropriate containers and instances for various machine learning tasks
• Train and optimize machine learning models
• Collaborate on projects at the intersection of research and product with a diverse, global team of researchers and engineers
• Support research through the construction of ML pipelines, prototypes, and reusable, testable code
• Process data and analyze feature extractions
• Analyze errors and provide solutions to problems
• Present results to collaborators and leadership