AI Research Scientist: AEC. London, UK

Position Overview

As an Applied AI Research Scientist at Autodesk Research, you will be doing 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.

This role will report to a Manager of Research Science in the AI Lab.

Responsibilities

• Develop new ML models and AI techniques
• Lead on research projects within a global team
• Review relevant AI/ML literature to identify emerging methods, technologies, and best practices
• Explore new data sources and discover techniques for best leveraging data

Minimum Qualifications

• A Master's or PhD in a field related to AI/ML such as: Computer Science, Mathematics, Statistics, Physics, Linguistics, Mechanical Engineering, Architecture or related disciplines
• Strong background applying Deep Learning techniques (including implementing custom architectures, optimizing model performance, developing novel loss functions, and deploying production-ready solutions)
• Familiarity in statistical methods for Machine Learning (e.g. Bayesian methods, HMMs, graphical models, dimension reduction, clustering, classification, regression techniques, etc.)
• Familiarity with PyTorch, TensorFlow, JAX or similar frameworks
• Strong coding abilities in Python

Preferred Qualifications

• Experience in the Architecture, Engineering, and/or Construction domains, including expertise with industry-specific data formats (e.g., BIM models, IFC files,

AEC Contract Documents and Drawings such as Drawing Sets, Specifications, or Shop Drawings)

• Knowledge of structured data representation and management in AEC workflows (building information modeling, structural specifications, project documentation)
• 2D & 3D Generative AI
• LLMs and Natural Language Processing
• Multi-modal deep learning and/or information retrieval
• Computational geometry and geometric methods (e.g. shape analysis, topology, differential geometry, discrete geometry, functional mapping, geometric deep learning, graph neural networks)
• Publication track record in machine learning conferences and/or journals
• Significant post-graduate research experience, or 5 or greater years of work experience (actual job title/position will be commensurate to experience)

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