Senior AI Research Engineer

Artificial intelligence is rewriting every industry it touches — but construction, the second largest sector in the global economy, has barely felt it yet. That's not a problem. That's an opening. ZeroRFI is the AI company building the intelligence layer for the built environment — the platform that makes every building project smarter, faster, and more predictable than the last. We're looking for a Principal Software Engineer to architect the systems that put AI at the center of how buildings get designed, built, and operated. This isn't AI as a feature. It's AI as the foundation.

We are seeking a Senior AI Engineer to lead the development of automated building design systems and advance the state-of-the-art in AI/ML applications for the built environment. You'll architect and implement generative design algorithms, computer vision systems, and optimization models that fundamentally transform how buildings are conceived, designed, and validated. This role sits at the intersection of deep learning, computational geometry, and architectural design, creating AI systems that augment human creativity while respecting engineering constraints and building codes.

This is a rare opportunity to apply cutting-edge AI research to one of humanity's most fundamental challenges: creating better spaces for people to live, work, and thrive.

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  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).

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  Develop computer vision pipelines for design and drawing analysis using modern frameworks like YOLO, SAM, and NeRF-based 3D reconstruction.

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  Build graph neural networks and geometric deep learning models for structural analysis and MEP (Mechanical, Electrical, Plumbing) system optimization.

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  Create reinforcement learning systems for multi-objective building optimization (energy efficiency, cost, occupant comfort, sustainability metrics).

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  Integrate AI models with industry-standard BIM tools (Revit, Rhino/Grasshopper) through custom APIs and plugins.

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  Deploy production ML pipelines using modern MLOps practices, including experiment tracking (Weights & Biases, MLflow), model versioning, and A/B testing frameworks.

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  Implement physics-informed neural networks for building performance simulation and predictive modeling.

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  Collaborate with architects and engineers to ensure AI systems produce practical, code-compliant, and constructible designs.

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  Lead research initiatives and publish findings to establish us as a thought leader in AEC AI innovation.

   

• 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.

   

• 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.