Design and implement generative AI models for automated building design, including floor plan generation, facade design, and structural optimization using state-of-the-art architectures (diffusion models, transformers, GANs).
Develop computer vision pipelines for design and drawing analysis using modern frameworks like YOLO, SAM, and NeRF-based 3D reconstruction.
Build graph neural networks and geometric deep learning models for structural analysis and MEP (Mechanical, Electrical, Plumbing) system optimization.
Create reinforcement learning systems for multi-objective building optimization (energy efficiency, cost, occupant comfort, sustainability metrics).
Integrate AI models with industry-standard BIM tools (Revit, Rhino/Grasshopper) through custom APIs and plugins.
Deploy production ML pipelines using modern MLOps practices, including experiment tracking (Weights & Biases, MLflow), model versioning, and A/B testing frameworks.
Implement physics-informed neural networks for building performance simulation and predictive modeling.
Collaborate with architects and engineers to ensure AI systems produce practical, code-compliant, and constructible designs.
Lead research initiatives and publish findings to establish us as a thought leader in AEC AI innovation.
Requirements
Master's degree or PhD in Computer Science, AI/ML, Computational Design, or related field (or equivalent industry experience).
3-5+ years of hands-on experience building and deploying ML models in production environments.
Deep expertise with modern deep learning frameworks (PyTorch preferred).
Strong foundation in computer vision, 3D geometry processing, and spatial reasoning algorithms.
Experience with generative AI models (VAEs, GANs, Diffusion Models, Transformers) and their practical applications.
Proficiency in Python and scientific computing libraries (NumPy, SciPy, scikit-learn, Open3D, trimesh).
Experience with cloud ML platforms (AWS SageMaker, Vertex AI, or Azure ML) and distributed training frameworks.
Understanding of optimization techniques (genetic algorithms, gradient-based optimization, constraint satisfaction).
Strong software engineering practices and experience with containerization (Docker) and orchestration (Kubernetes).
Excellent communication skills to translate complex AI concepts to domain experts and stakeholders.
Tech Stack
AWS
Azure
Cloud
Docker
Kubernetes
Numpy
Python
PyTorch
Scikit-Learn
Benefits
The opportunity to define and build AI systems that will reshape a $10 trillion global industry.
Access to unique datasets and real-world problems at the intersection of AI and the built environment.
Collaboration with leading architects, engineers, and construction professionals who are eager to embrace AI transformation.
Resources to pursue cutting-edge research while maintaining a focus on practical, deployable solutions.
Mentorship from industry veterans who understand both the technical and business aspects of AEC technology.
The freedom to experiment with emerging AI architectures and techniques in a high-impact domain.