PlanChecker.AI
Artificial Intelligence Engineer
PlanChecker.AI, Los Angeles, California, United States, 90079
Overview
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 permitsensuring submissions are accurate, compliant, and complete before theyre submitted. This leads to faster approvals, fewer revisions, reduced red tape, and enhanced clarity and collaboration across stakeholders. Key capabilities
AI-powered compliance checks across architectural, structural, mechanical, electrical, plumbing, and fire safety codes Real-time verification by overlaying approved plans on-site via smart devices, reducing construction errors Continuous monitoring of alterations, zoning law changes, and public records to detect unauthorized modifications Material estimation, cost analysis, and supplier pricing insights to optimize budgeting and reduce waste Recommendations for energy-efficient design, code-compliant safety features, and long-term maintenance to enhance sustainability and resilience 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 Responsibilities
Architect, 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.AIs 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 usageespecially 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: Bachelors or Masters 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 processingplus 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.
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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 permitsensuring submissions are accurate, compliant, and complete before theyre submitted. This leads to faster approvals, fewer revisions, reduced red tape, and enhanced clarity and collaboration across stakeholders. Key capabilities
AI-powered compliance checks across architectural, structural, mechanical, electrical, plumbing, and fire safety codes Real-time verification by overlaying approved plans on-site via smart devices, reducing construction errors Continuous monitoring of alterations, zoning law changes, and public records to detect unauthorized modifications Material estimation, cost analysis, and supplier pricing insights to optimize budgeting and reduce waste Recommendations for energy-efficient design, code-compliant safety features, and long-term maintenance to enhance sustainability and resilience 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 Responsibilities
Architect, 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.AIs 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 usageespecially 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: Bachelors or Masters 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 processingplus 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.
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