Senior AI Research Engineer

Key Responsibilities

• 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.

Preferred Qualifications

• Experience with computational design tools (Grasshopper, Dynamo) and parametric modeling.
• Familiarity with building information modeling (BIM) standards and IFC data schemas.
• Knowledge of graph neural networks (PyTorch Geometric, DGL) for structural and spatial analysis.
• Experience with physics simulation engines (Mujoco, Isaac Sim) or FEA integration.
• Background in multi-agent reinforcement learning for complex system optimization.
• Contributions to open-source ML projects or published research in relevant venues (NeurIPS, ICML, CVPR, or domain-specific conferences).
• Experience with point cloud processing and 3D scene understanding (PointNet++, DGCNN).
• Understanding of construction workflows and building codes.