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About the Role

ZeroRFI is seeking an AI Engineer to lead the development of automated building design systems and advance AI/ML applications for the built environment. You will architect and implement generative design algorithms, computer vision systems, and optimization models to transform building conception, design, and validation. This role combines deep learning, computational geometry, and architectural design to create AI systems that augment human creativity while adhering to engineering constraints and building codes. This is an opportunity to apply cutting-edge AI research to create better spaces for living, working, and thriving.

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 ZeroRFI 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.
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.

Skills

Generative AI models (diffusion models, transformers, GANs)
Computer vision pipelines (YOLO, SAM, NeRF)
Graph neural networks
Geometric deep learning
Reinforcement learning
BIM tools (Revit, Rhino/Grasshopper)
MLOps practices
Physics-informed neural networks
Python
PyTorch
NumPy
SciPy
scikit-learn
Open3D
trimesh
AWS SageMaker
Vertex AI
Azure ML
Docker
Kubernetes
Computational design tools (Grasshopper, Dynamo)
BIM standards
IFC data schemas
PyTorch Geometric
DGL
Mujoco
Isaac Sim
Point cloud processing
3D scene understanding (PointNet++, DGCNN)

Location

San Francisco
Atlanta

Work Type

Hybrid

Experience Level

Senior
Principal

Education Level

Master's degree or PhD in Computer Science, AI/ML, Computational Design, or related field (or equivalent industry experience)

Salary/Compensations

$250,000–$300,000

Benefits

Meaningful early-stage equity
One-time home office stipend
Comprehensive healthcare coverage with multiple plan options
Unlimited PTO
Multiple investment vehicles for retirement planning
Curated toolkit of AI, development, and research resources
Direct exposure to ZeroRFI's owner and developer community

About the Company

Artificial intelligence is rewriting every industry it touches — but construction, the second largest sector in the global economy, has barely felt it yet.
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.
This isn't AI as a feature. It's AI as the foundation.