The Nuclear Company
The Nuclear Company is the fastest growing startup in the nuclear and energy space creating a never before seen fleet‑scale approach to building nuclear reactors. Through its design‑once, build‑many approach and coalition building across communities, regulators, and financial stakeholders, The Nuclear Company is committed to delivering safe and reliable electricity at the lowest cost, while catalyzing the nuclear industry toward rapid development in America and globally.
About The Role The Principal Vision Developer is a senior technical leadership role responsible for designing, developing, and deploying advanced computer vision and AI‑powered visual systems that transform nuclear construction, safety monitoring, and quality control. This position requires deep expertise in machine learning, image processing, camera systems integration, and real‑time video analytics to enable automated progress tracking, safety compliance monitoring, and intelligent inspection systems for Nuclear OS. You’ll work at the intersection of artificial intelligence and nuclear infrastructure, helping to automate visual inspection, detect anomalies, and ensure construction quality through cutting‑edge computer vision technology.
Key Responsibilities Computer Vision System Development
Design and develop AI‑powered computer vision systems for automated construction progress monitoring, comparing current site images to BIM models to determine completion percentages
Build real‑time video analytics systems that analyze camera feeds and drone imagery to detect construction issues, missing components, and deviations from design
Develop automated inspection systems using AI and computer vision to verify construction quality and detect deviations
Create computer vision algorithms for anomaly detection, defect identification, and automated image analysis from drone and camera feeds
Implement 3D reconstruction from image sequences and point clouds for digital twin development
Camera Systems Integration & Detection
Design and deploy multi‑camera networks across construction sites for comprehensive visual coverage
Integrate AI‑enabled cameras into IoT sensor networks for real‑time data collection and analysis
Implement camera calibration systems for accurate geometric measurements and 3D reconstruction
Develop multi‑camera synchronization for stereo vision and 360‑degree coverage
Build camera management systems for configuration, monitoring, and maintenance of distributed camera networks
Integrate various camera types including fixed surveillance cameras, PTZ cameras, thermal/infrared cameras, and mobile drone cameras
Design detection pipelines that process video streams from multiple cameras simultaneously
Implement intelligent camera placement strategies to optimize coverage and minimize blind spots
Create camera health monitoring systems to detect and alert on camera failures or degraded performance
Develop bandwidth optimization for efficient video streaming from large camera networks
Safety & Compliance Monitoring
Develop AI safety enforcement systems that analyze camera feeds in real time to detect PPE compliance (helmets, vests, safety glasses)
Build perimeter monitoring systems that detect unauthorized personnel or equipment breaches during critical operations like heavy lifts
Create automated alert systems that log safety violations and notify supervisors immediately when non‑compliance is detected
Develop hazard detection models that identify unsafe conditions, obstructions, or potential safety risks in construction zones
Implement worker tracking systems using camera‑based detection for safety monitoring and emergency response
Build tailgating detection systems that identify when multiple people enter on a single badge swipe
Progress Tracking & Quality Control
Build automated progress assessment systems using computer vision to track work package completion
Develop material classification algorithms that automatically identify installed components and materials from construction site images
Create surveillance video analysis systems to detect and track work packages’ components automatically
Implement activity recognition models (using YOLO, CNNs) to monitor construction activities and assess task completion times
Build defect detection systems for automated quality inspection of welds, concrete pours, and structural installations
Develop visual comparison tools that overlay as‑built conditions against design models to identify discrepancies
Create truck and material arrival detection using camera‑based AI at jobsite entrance gates
Machine Learning & AI Model Development
Train deep learning models (CNNs, transformers, YOLO, R‑CNN) for object detection, segmentation, and classification in construction environments
Develop custom datasets from drone imagery, site cameras, and inspection photos with proper labeling and annotation
Implement transfer learning to adapt pre‑trained models for nuclear construction‑specific applications
Create ensemble models that combine multiple vision techniques for robust performance
Optimize models for real‑time inference on edge devices and GPU‑accelerated servers
Implement continuous learning pipelines that improve model accuracy over time with new data
Build convolutional neural network‑based detection systems for automated identification and classification
Integration & Deployment
Integrate computer vision systems with Nuclear OS platform for seamless data flow and automated reporting
Deploy vision models on edge devices, cloud infrastructure, and hybrid architecture
Connect to IoT camera networks and drone feeds for real‑time image acquisition
Build APIs and microservices for vision system integration with other Nuclear OS modules
Implement data pipelines for image ingestion, preprocessing, inference, and result storage
Create visualization dashboards showing real‑time progress metrics, safety alerts, and quality issues
Integrate with access control systems for enhanced security monitoring
Design isolated security networks for camera systems in sensitive areas
Robotic Vision & Automation
Develop computer vision systems for robotic perception and autonomous navigation
Integrate vision systems with robotic inspection and quality control platforms
Build visual servoing systems for robot guidance and manipulation tasks
Develop 3D pose estimation for robotic pick‑and‑place operations
Create visual SLAM systems for autonomous robot navigation in construction sites
Advanced Vision Applications
Implement photogrammetry pipelines for 3D reconstruction from drone and camera imagery
Develop infrared and thermal imaging analysis for equipment monitoring and anomaly detection
Build optical character recognition (OCR) systems for automated document processing and equipment labeling
Create facial recognition systems for access control and personnel tracking (where permitted)
Develop appearance‑based search for security and incident investigation across multiple camera feeds
Implement vehicle and individual tracking across facility camera networks for security analysis
Technical Leadership & Innovation
Provide expert guidance on computer vision architecture, camera systems, algorithms, and best practices
Lead cross‑functional teams involving data scientists, software engineers, network engineers, and domain experts
Evaluate emerging vision technologies including foundation models, vision transformers, and multimodal AI
Establish development standards for vision system deployment in nuclear environments
Mentor junior developers on machine learning, computer vision, and AI techniques
Represent TNC in industry forums on AI and computer vision applications
Drive innovation in automated construction monitoring and intelligent inspection systems
Required Qualifications Education & Experience
Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or related field (Master’s or Ph.D. preferred)
12+ years of experience in computer vision, machine learning, or AI development
5+ years in a senior or lead role with demonstrated technical leadership
Experience in industrial, construction, or manufacturing applications preferred
Technical Skills - Computer Vision
Expert proficiency in computer vision libraries (OpenCV, scikit‑image, PIL/Pillow)
Deep understanding of classical CV techniques (edge detection, feature extraction, optical flow, SLAM)
Experience with 3D computer vision (stereo vision, structure from motion, photogrammetry)
Knowledge of image processing techniques (filtering, segmentation, morphological operations)
Proficiency in video analytics and real‑time processing
Understanding of camera calibration, lens distortion correction, and geometric transformations
Technical Skills - Camera Systems & Hardware
Strong experience with camera integration, configuration, and management
Knowledge of camera protocols (ONVIF, RTSP, HTTP streaming, GigE Vision)
Understanding of camera specifications (resolution, frame rate, dynamic range, sensor types)
Experience with various camera types (IP cameras, thermal cameras, PTZ cameras, industrial cameras)
Proficiency in multi‑camera synchronization and calibration
Knowledge of video compression standards (H.264, H.265, MJPEG)
Understanding of network bandwidth requirements and optimization for video streaming
Familiarity with camera mounting, positioning, and field‑of‑view calculations
Technical Skills - Machine Learning & Deep Learning
Expert knowledge of deep learning frameworks (PyTorch, TensorFlow, Keras)
Strong experience with CNNs, vision transformers, and modern architectures (ResNet, YOLO, Mask R‑CNN, ViT)
Proficiency in object detection, semantic segmentation, and instance segmentation
Experience with model training, hyperparameter tuning, and optimization
Knowledge of transfer learning, fine‑tuning, and domain adaptation
Understanding of data augmentation, regularization, and model evaluation techniques
Technical Skills - Programming & Development
Expert programming skills in Python (primary) and C++ (for performance‑critical code)
Proficiency in GPU programming (CUDA, cuDNN) for accelerated inference
Experience with MLOps tools (MLflow, Weights & Biases, TensorBoard)
Knowledge of containerization (Docker, Kubernetes) for model deployment
Familiarity with cloud platforms (AWS SageMaker, Azure ML, Google Cloud AI)
Understanding of REST APIs, microservices, and distributed systems
Technical Skills - Data & Infrastructure
Experience with large‑scale image datasets and data annotation tools (Labelbox, CVAT, Label Studio)
Knowledge of data pipelines, ETL processes, and data versioning (DVC)
Proficiency in database systems for image metadata and results storage
Understanding of edge computing and model optimization (TensorRT, ONNX, quantization)
Familiarity with streaming video processing and real‑time analytics
Experience with video management systems (VMS) and network video recorders (NVR)
Domain Knowledge
Understanding of construction processes, quality control, and safety requirements
Knowledge of BIM models, CAD data, and 3D visualization
Familiarity with industrial inspection and automated monitoring systems
Experience with regulatory compliance and documentation requirements
Understanding of nuclear facility operations and safety protocols (preferred)
Knowledge of security systems and surveillance best practices
Soft Skills
Excellent problem‑solving abilities with creative approach to vision challenges
Strong communication skills to explain complex AI concepts to diverse stakeholders
Project management experience delivering production vision systems
Ability to work collaboratively across engineering, operations, security, and safety teams
Strategic thinking with focus on scalable, maintainable solutions
Attention to detail for accuracy‑critical applications
Preferred Qualifications
Ph.D. in Computer Vision, Machine Learning, or related field
Publications in top‑tier computer vision conferences (CVPR, ICCV, ECCV)
Experience in nuclear, energy, or highly regulated industries
Background in robotics, autonomous systems, or industrial automation
Knowledge of 3D sensors (LiDAR, depth cameras, structured light)
Experience with multi‑camera systems and camera networks
Familiarity with real‑time operating systems and embedded vision
Certifications in AI/ML (AWS ML Specialty, Google ML Engineer, etc.)
Contributions to open‑source computer vision projects
Experience with video surveillance systems and security analytics
Knowledge of lighting design for optimal camera performance
Benefits
Competitive compensation packages
401k with company match
Medical, dental, vision plans
Generous vacation policy, plus holidays
Estimated Starting Salary Range The estimated starting salary range for this role is $198,000–$228,000 annually less applicable withholdings and deductions, paid on a bi‑weekly basis. The actual salary offered may vary based on relevant factors as determined in the Company’s discretion, which may include experience, qualifications, tenure, skill set, availability of qualified candidates, geographic location, certifications held, and other criteria deemed pertinent to the particular role.
EEO Statement The Nuclear Company is an equal opportunity employer committed to fostering an environment of inclusion in the workplace. We provide equal employment opportunities to all qualified applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We prohibit discrimination in all aspects of employment, including hiring, promotion, demotion, transfer, compensation, and termination.
Export Control Certain positions at The Nuclear Company may involve access to information and technology subject to export controls under U.S. law. Compliance with these export controls may result in The Nuclear Company limiting its consideration of certain applicants.
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Engineering and Information Technology
Industries
Nuclear Electric Power Generation
#J-18808-Ljbffr
About The Role The Principal Vision Developer is a senior technical leadership role responsible for designing, developing, and deploying advanced computer vision and AI‑powered visual systems that transform nuclear construction, safety monitoring, and quality control. This position requires deep expertise in machine learning, image processing, camera systems integration, and real‑time video analytics to enable automated progress tracking, safety compliance monitoring, and intelligent inspection systems for Nuclear OS. You’ll work at the intersection of artificial intelligence and nuclear infrastructure, helping to automate visual inspection, detect anomalies, and ensure construction quality through cutting‑edge computer vision technology.
Key Responsibilities Computer Vision System Development
Design and develop AI‑powered computer vision systems for automated construction progress monitoring, comparing current site images to BIM models to determine completion percentages
Build real‑time video analytics systems that analyze camera feeds and drone imagery to detect construction issues, missing components, and deviations from design
Develop automated inspection systems using AI and computer vision to verify construction quality and detect deviations
Create computer vision algorithms for anomaly detection, defect identification, and automated image analysis from drone and camera feeds
Implement 3D reconstruction from image sequences and point clouds for digital twin development
Camera Systems Integration & Detection
Design and deploy multi‑camera networks across construction sites for comprehensive visual coverage
Integrate AI‑enabled cameras into IoT sensor networks for real‑time data collection and analysis
Implement camera calibration systems for accurate geometric measurements and 3D reconstruction
Develop multi‑camera synchronization for stereo vision and 360‑degree coverage
Build camera management systems for configuration, monitoring, and maintenance of distributed camera networks
Integrate various camera types including fixed surveillance cameras, PTZ cameras, thermal/infrared cameras, and mobile drone cameras
Design detection pipelines that process video streams from multiple cameras simultaneously
Implement intelligent camera placement strategies to optimize coverage and minimize blind spots
Create camera health monitoring systems to detect and alert on camera failures or degraded performance
Develop bandwidth optimization for efficient video streaming from large camera networks
Safety & Compliance Monitoring
Develop AI safety enforcement systems that analyze camera feeds in real time to detect PPE compliance (helmets, vests, safety glasses)
Build perimeter monitoring systems that detect unauthorized personnel or equipment breaches during critical operations like heavy lifts
Create automated alert systems that log safety violations and notify supervisors immediately when non‑compliance is detected
Develop hazard detection models that identify unsafe conditions, obstructions, or potential safety risks in construction zones
Implement worker tracking systems using camera‑based detection for safety monitoring and emergency response
Build tailgating detection systems that identify when multiple people enter on a single badge swipe
Progress Tracking & Quality Control
Build automated progress assessment systems using computer vision to track work package completion
Develop material classification algorithms that automatically identify installed components and materials from construction site images
Create surveillance video analysis systems to detect and track work packages’ components automatically
Implement activity recognition models (using YOLO, CNNs) to monitor construction activities and assess task completion times
Build defect detection systems for automated quality inspection of welds, concrete pours, and structural installations
Develop visual comparison tools that overlay as‑built conditions against design models to identify discrepancies
Create truck and material arrival detection using camera‑based AI at jobsite entrance gates
Machine Learning & AI Model Development
Train deep learning models (CNNs, transformers, YOLO, R‑CNN) for object detection, segmentation, and classification in construction environments
Develop custom datasets from drone imagery, site cameras, and inspection photos with proper labeling and annotation
Implement transfer learning to adapt pre‑trained models for nuclear construction‑specific applications
Create ensemble models that combine multiple vision techniques for robust performance
Optimize models for real‑time inference on edge devices and GPU‑accelerated servers
Implement continuous learning pipelines that improve model accuracy over time with new data
Build convolutional neural network‑based detection systems for automated identification and classification
Integration & Deployment
Integrate computer vision systems with Nuclear OS platform for seamless data flow and automated reporting
Deploy vision models on edge devices, cloud infrastructure, and hybrid architecture
Connect to IoT camera networks and drone feeds for real‑time image acquisition
Build APIs and microservices for vision system integration with other Nuclear OS modules
Implement data pipelines for image ingestion, preprocessing, inference, and result storage
Create visualization dashboards showing real‑time progress metrics, safety alerts, and quality issues
Integrate with access control systems for enhanced security monitoring
Design isolated security networks for camera systems in sensitive areas
Robotic Vision & Automation
Develop computer vision systems for robotic perception and autonomous navigation
Integrate vision systems with robotic inspection and quality control platforms
Build visual servoing systems for robot guidance and manipulation tasks
Develop 3D pose estimation for robotic pick‑and‑place operations
Create visual SLAM systems for autonomous robot navigation in construction sites
Advanced Vision Applications
Implement photogrammetry pipelines for 3D reconstruction from drone and camera imagery
Develop infrared and thermal imaging analysis for equipment monitoring and anomaly detection
Build optical character recognition (OCR) systems for automated document processing and equipment labeling
Create facial recognition systems for access control and personnel tracking (where permitted)
Develop appearance‑based search for security and incident investigation across multiple camera feeds
Implement vehicle and individual tracking across facility camera networks for security analysis
Technical Leadership & Innovation
Provide expert guidance on computer vision architecture, camera systems, algorithms, and best practices
Lead cross‑functional teams involving data scientists, software engineers, network engineers, and domain experts
Evaluate emerging vision technologies including foundation models, vision transformers, and multimodal AI
Establish development standards for vision system deployment in nuclear environments
Mentor junior developers on machine learning, computer vision, and AI techniques
Represent TNC in industry forums on AI and computer vision applications
Drive innovation in automated construction monitoring and intelligent inspection systems
Required Qualifications Education & Experience
Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or related field (Master’s or Ph.D. preferred)
12+ years of experience in computer vision, machine learning, or AI development
5+ years in a senior or lead role with demonstrated technical leadership
Experience in industrial, construction, or manufacturing applications preferred
Technical Skills - Computer Vision
Expert proficiency in computer vision libraries (OpenCV, scikit‑image, PIL/Pillow)
Deep understanding of classical CV techniques (edge detection, feature extraction, optical flow, SLAM)
Experience with 3D computer vision (stereo vision, structure from motion, photogrammetry)
Knowledge of image processing techniques (filtering, segmentation, morphological operations)
Proficiency in video analytics and real‑time processing
Understanding of camera calibration, lens distortion correction, and geometric transformations
Technical Skills - Camera Systems & Hardware
Strong experience with camera integration, configuration, and management
Knowledge of camera protocols (ONVIF, RTSP, HTTP streaming, GigE Vision)
Understanding of camera specifications (resolution, frame rate, dynamic range, sensor types)
Experience with various camera types (IP cameras, thermal cameras, PTZ cameras, industrial cameras)
Proficiency in multi‑camera synchronization and calibration
Knowledge of video compression standards (H.264, H.265, MJPEG)
Understanding of network bandwidth requirements and optimization for video streaming
Familiarity with camera mounting, positioning, and field‑of‑view calculations
Technical Skills - Machine Learning & Deep Learning
Expert knowledge of deep learning frameworks (PyTorch, TensorFlow, Keras)
Strong experience with CNNs, vision transformers, and modern architectures (ResNet, YOLO, Mask R‑CNN, ViT)
Proficiency in object detection, semantic segmentation, and instance segmentation
Experience with model training, hyperparameter tuning, and optimization
Knowledge of transfer learning, fine‑tuning, and domain adaptation
Understanding of data augmentation, regularization, and model evaluation techniques
Technical Skills - Programming & Development
Expert programming skills in Python (primary) and C++ (for performance‑critical code)
Proficiency in GPU programming (CUDA, cuDNN) for accelerated inference
Experience with MLOps tools (MLflow, Weights & Biases, TensorBoard)
Knowledge of containerization (Docker, Kubernetes) for model deployment
Familiarity with cloud platforms (AWS SageMaker, Azure ML, Google Cloud AI)
Understanding of REST APIs, microservices, and distributed systems
Technical Skills - Data & Infrastructure
Experience with large‑scale image datasets and data annotation tools (Labelbox, CVAT, Label Studio)
Knowledge of data pipelines, ETL processes, and data versioning (DVC)
Proficiency in database systems for image metadata and results storage
Understanding of edge computing and model optimization (TensorRT, ONNX, quantization)
Familiarity with streaming video processing and real‑time analytics
Experience with video management systems (VMS) and network video recorders (NVR)
Domain Knowledge
Understanding of construction processes, quality control, and safety requirements
Knowledge of BIM models, CAD data, and 3D visualization
Familiarity with industrial inspection and automated monitoring systems
Experience with regulatory compliance and documentation requirements
Understanding of nuclear facility operations and safety protocols (preferred)
Knowledge of security systems and surveillance best practices
Soft Skills
Excellent problem‑solving abilities with creative approach to vision challenges
Strong communication skills to explain complex AI concepts to diverse stakeholders
Project management experience delivering production vision systems
Ability to work collaboratively across engineering, operations, security, and safety teams
Strategic thinking with focus on scalable, maintainable solutions
Attention to detail for accuracy‑critical applications
Preferred Qualifications
Ph.D. in Computer Vision, Machine Learning, or related field
Publications in top‑tier computer vision conferences (CVPR, ICCV, ECCV)
Experience in nuclear, energy, or highly regulated industries
Background in robotics, autonomous systems, or industrial automation
Knowledge of 3D sensors (LiDAR, depth cameras, structured light)
Experience with multi‑camera systems and camera networks
Familiarity with real‑time operating systems and embedded vision
Certifications in AI/ML (AWS ML Specialty, Google ML Engineer, etc.)
Contributions to open‑source computer vision projects
Experience with video surveillance systems and security analytics
Knowledge of lighting design for optimal camera performance
Benefits
Competitive compensation packages
401k with company match
Medical, dental, vision plans
Generous vacation policy, plus holidays
Estimated Starting Salary Range The estimated starting salary range for this role is $198,000–$228,000 annually less applicable withholdings and deductions, paid on a bi‑weekly basis. The actual salary offered may vary based on relevant factors as determined in the Company’s discretion, which may include experience, qualifications, tenure, skill set, availability of qualified candidates, geographic location, certifications held, and other criteria deemed pertinent to the particular role.
EEO Statement The Nuclear Company is an equal opportunity employer committed to fostering an environment of inclusion in the workplace. We provide equal employment opportunities to all qualified applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We prohibit discrimination in all aspects of employment, including hiring, promotion, demotion, transfer, compensation, and termination.
Export Control Certain positions at The Nuclear Company may involve access to information and technology subject to export controls under U.S. law. Compliance with these export controls may result in The Nuclear Company limiting its consideration of certain applicants.
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Engineering and Information Technology
Industries
Nuclear Electric Power Generation
#J-18808-Ljbffr