Artificial Intelligence Engineer

About PlanChecker.AI

PlanChecker.AI is a pioneering AI-driven platform that reimagines how building plans get approved. Designed for both applicants and city officials, it streamlines plan reviews, code compliance, and permits—ensuring submissions are accurate, compliant, and complete before they're submitted. This leads to faster approvals, fewer revisions, reduced red tape, and enhanced clarity and collaboration across

Key capabilities include:

• AI-powered compliance checks across architectural, structural, mechanical, electrical, plumbing, and fire safety
• Real-time verification by overlaying approved plans on-site via smart devices, reducing construction
• Continuous monitoring of alterations, zoning law changes, and public records to detect unauthorized
• Material estimation, cost analysis, and supplier pricing insights to optimize budgeting and reduce
• Recommendations for energy-efficient design, code-compliant safety features, and long-term maintenance to enhance sustainability and

Role Overview

We're looking for a motivated AI Engineer to help scale and refine the intelligence behind PlanChecker.AI. You'll build and deploy machine learning models, improve system performance, and ensure our platform delivers fast, reliable, and highly accurate results for complex permit workflows. You'll collaborate closely with product, design, and engineering teams to create solutions that align with real-world needs in the construction and regulatory domains.

• Key ResponsibilitiesArchitect, train, evaluate, and optimize machine learning and deep learning models for plan review, code compliance, overlay verification, change detection, and cost/material estimation pipelines.
• Collaborate with software engineers to effectively integrate AI models within PlanChecker.AI's platform and user workflows.
• Develop and maintain data pipelines for truth generation, labeling, validation, and ingestion across architectural, structural, and regulatory datasets.
• Implement rigorous evaluation metrics to measure model performance for accuracy, latency, scalability, fairness, and robustness.
• Drive improvements in real-time plan verification features by optimizing inference latency and resource usage—especially for mobile and on-site overlays.
• Expand and enhance the alteration monitoring engine, tracking zoning regulation changes and plan compliance continuously using public records and spatial data.
• Support and refine cost analysis and sustainability recommendation engines to inform material planning and energy-efficient design.
• Keep abreast of ML trends relevant to compliance, construction, computer vision, NLP, geospatial AI, and foundation model tuning.
• Champion best practices in MLOps, model versioning, CI/CD, A/B testing, monitoring, and observability.

Qualifications

Required:

• Bachelor's or Master's degree in Computer Science, AI, Machine Learning, Data Science, or closely related fields.
• 3+ years of hands-on ML/AI engineering experience, especially in applied production environments.
• Proficiency in Python and major ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Strong system-level design understanding and experience with APIs and microservices.
• Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and deploying models in scalable production environments.
• Comfortable with startups: fast iterations, ambiguous problem spaces, and collaborative innovation.

Preferred:

• Demonstrated ability in computer vision (e.g., image overlays), NLP, or geospatial data processing—plus interest in generative or foundation models.
• Experience developing or tuning LLMs or foundation models.
• Expertise in anomaly detection, real-time inference optimization, or overlay/matching in vision systems.
• Knowledge of MLOps platforms (e.g. MLflow, Kubeflow, Weights & Biases).
• Domain experience in architecture, engineering, construction, permitting, or public administration contexts.
• Exposure to Geospatial or GIS technologies.