Senior AI Research Engineer
$250,000–$300,000 year
HybridSan Francisco, California, United States
Job Summary
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. Develop computer vision pipelines for design and drawing analysis, build graph neural networks for structural analysis, and create reinforcement learning systems for multi-objective building optimization. Integrate AI models with industry-standard BIM tools through custom APIs and deploy production ML pipelines using modern MLOps practices. Collaborate with architects and engineers to ensure AI systems produce practical, code-compliant designs while leading research initiatives to publish findings.
Required Qualifications
- 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
Desired 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
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