Top Computer Vision Jobs in Denver & Boulder, CO

Develop production-quality computer vision, image processing, machine learning, and geospatial algorithms for imagery and spatial data. Responsibilities include mathematical analysis, algorithm design and validation, software development, large-dataset analysis, system optimization, technical collaboration, code reviews, architecture discussions, and transitioning research prototypes into scalable AI-enabled workflows.

Architect and deploy computer vision and perception systems for space-based missile defense. Develop C++ multi-band EO/IR image processing, physics-based feature extraction, object detection, tracking, and discrimination algorithms. Model infrared phenomenology, validate algorithms through high-fidelity software- and hardware-in-the-loop simulations, perform statistical performance analysis, and integrate reliable flight software for space-qualified processors.

Build and ship computer-vision models end-to-end: dataset curation, training, deployment, and monitoring across cloud and roadside edge hardware. Optimize for high-volume inference, accuracy, efficiency, and real-world robustness while collaborating cross-functionally and iterating rapidly.

Researches, designs, develops, trains, and deploys computer vision and machine learning models. Builds and maintains ML software, testing and validation processes, and documentation. Applies image and video processing, deep learning, and computer vision techniques across edge and cloud-based platforms while improving existing applications and performance.

Leads researchers and developers in building computer vision AI/ML solutions for remote sensing, including automatic target recognition and multimodal EO/SAR data fusion. Responsibilities include preparing datasets, researching supervised and unsupervised methods, training and optimizing deep learning models, deploying algorithms to cloud environments, communicating technical findings, and documenting systems. The role requires active TS/SCI clearance and provides technical leadership from prototype through operational deployment.

Build and maintain Python-based intelligent automation for healthcare revenue cycle management. Develop OpenCV and image-based RPA for legacy desktop and virtualized applications, OCR document extraction, and Selenium web automation. Integrate with EHR, billing, and payer systems; apply AI/ML techniques; use Azure services; and ensure HIPAA/HITRUST-compliant security, monitoring, documentation, and credential handling. Collaborate with technical and operations teams while independently delivering production automation modules.

Perform image segmentation and object labeling for computer vision datasets using polygons, bounding boxes, and semantic segmentation. Follow annotation guidelines, complete quality checks, meet daily productivity targets, communicate unclear tasks to team leads, and suggest workflow improvements. Work remotely with datasets involving roads, vehicles, satellite imagery, blueprints, roofing, and related visual content.

Review and annotate robotic-system video footage by identifying objects, movements, events, visual features, and anomalies. Provide feedback to improve video datasets and annotation consistency, collaborate with AI teams on annotation protocols, document observations, participate in quality reviews, and recommend improvements to video capture and labeling strategies.

Develop, optimize, and deploy production-grade computer vision and deep learning models for real-time perception and navigation on edge hardware. Integrate AI systems with robots, drones, unmanned systems, and swarms while optimizing inference with CUDA and TensorRT. Manage containerized deployments using Docker and Kubernetes, contribute to CI/CD and automated testing, and transition research prototypes into reliable production features.

Perform object detection annotation on visual datasets using digital annotation tools. Label and review objects, apply project guidelines, correct inconsistencies, maintain data quality and confidentiality, and meet productivity standards. The role requires accuracy during repetitive tasks, independent remote work, collaboration with project teams, and a consistent 40-hour weekly schedule.