.As an AI Research Scientist at Autodesk Research, you will be doing fundamental and applied research that will help our customers imagine, design, and make a better world. We are a team of scientists, researchers, engineers, and designers working together on projects that range from learning-based design systems, computer vision, graphics, robotics, human-computer interaction, sustainability, simulation, manufacturing, architectural design and construction.As a member of the AI Lab in Autodesk Research you will be an expert in research areas such as artificial intelligence, deep learning, generative AI, machine learning, computer vision, reinforcement learning, information retrieval, and natural language processing.Autodesk's AI Lab is active in the wider research community, targeting publications at CVPR, NeurIPS, ICML, ICLR, SIGGRAPH, and other top-tier conferences. We collaborate with top academic & industry labs, combining the best of an academic environment with product-guided research. We are a global team, located in London, San Francisco, Toronto, and remotely.ResponsibilitiesExplore and develop new ML models and AI techniquesLead and collaborate on research projects within a global teamReview relevant AI/ML literature to identify emerging methods, technologies, and best practicesWork towards long-term research goals, while identifying intermediate milestonesBuild relationships and collaborate with academics and institutionsExplore new data sources and discover techniques for best leveraging dataPublish papers; attend and speak at conferencesThink strategically about research directionsCompany:AutodeskQualifications:Minimum QualificationsA Master's or PhD in a field related to AI/ML such as: Computer Science, Mathematics, Statistics, Physics, Linguistics, Mechanical Engineering, Architecture or related disciplinesPublication track record in machine learning conferences and/or journalsSignificant post-graduate research experience, or 5 or greater years of work experience (actual job title/position will be commensurate to experience)Good background in statistical methods for Machine Learning (e.G. Bayesian methods, HMMs, graphical models, dimension reduction, clustering, classification, regression techniques, etc.)Familiarity with Deep Learning techniques (e.G. Network architectures, regularization techniques, learning techniques, loss-functions, optimization strategies, etc.)Familiarity with PyTorch, TensorFlow, JAX or similar frameworksStrong coding abilities in Python and/or C++Preferred QualificationsLLMs and Natural Language Processing2D & 3D Generative AIReinforcement LearningComputational geometry and geometric methods (e.G
