Top Computer Vision Jobs in Denver & Boulder, CO

Build and deploy computer vision and machine learning solutions for power grid infrastructure, including defect detection, thermal anomalies, vegetation monitoring, and intrusion surveillance. Responsibilities span research implementation, model experimentation, error analysis, data pipelines, experiment tracking, model serving, testing, monitoring, and client delivery. The role requires productionizing models, adapting research papers, optimizing architectures, and independently owning projects from problem definition through deployment.

Leads advanced computer vision R&D and production engineering for real-time retail edge deployments. Designs and optimizes deep learning, computer vision, and vision-language models; validates algorithms through evidence-based analysis; and deploys high-throughput, low-latency ML pipelines. Responsibilities include TensorRT and CUDA optimization, Docker-based cloud and embedded deployment, image registration and warping, and translating fundamental research into production-grade systems while providing technical leadership.

Develop and transition computer vision and image processing algorithms from prototypes to production-ready spacecraft systems. Design tracking, navigation, estimation, filtering, uncertainty-analysis, and optimization algorithms; validate performance through simulations, benchmarks, and real-world sensor data; assess camera and optical hardware impacts; and define APIs for integration with software and spacecraft architectures.

Design real-world computer vision tasks, build reference solutions and evaluation components in Python, assess AI model outputs, identify failure modes, and develop structured rubrics and guidelines. Collaborate with experts to maintain high-quality training data and evaluation standards for multimodal and generative vision systems. Part-time, remote contractor role (~20 hours/week).

Senior product engineer owning end-to-end product surfaces for a computer vision platform. Ship features from idea to production, prioritize roadmap, design UX details, integrate APIs, improve annotation and model-assisted labeling, and collaborate with engineering and users to drive product impact.

Design, improve, and deploy production-grade computer vision systems for sports video: detection, multi-object tracking, camera calibration, homography, field registration, identity association, and robustness across varied cameras and video quality. Lead experiments, adapt models to new domains, collaborate with data and engineering teams, and drive end-to-end solutions from prototype to production.

Leads the technical direction of an autonomous vehicle perception stack. Responsibilities include architecting scalable perception systems, developing scene detection and runtime activation, improving robustness against sensor degradation and adverse weather, integrating multimodal models, managing ML and data pipelines, monitoring field performance, leading testing and feature delivery, and mentoring engineers. Requires strong robotics, computer vision, machine learning, real-time systems, software architecture, and team leadership experience.

Develop and optimize computer-vision and deep-learning models (segmentation, depth) and end-to-end ML pipelines for perception. Own lifecycle from prototype to production deployment on edge and cloud, evaluate performance on large datasets, and collaborate with cross-functional teams to integrate perception into autonomous tractors.

Design and train foundation vision-language models that read entire CT studies end-to-end. Build custom PyTorch architectures and distributed training pipelines, run large-scale experiments, evaluate models for clinical safety, and move models through FDA submission to hospital deployment.

As a Computer Vision Applied Research Scientist, you'll design novel architectures for understanding construction drawings, conduct end-to-end experiments, and transition models to production. You'll collaborate with others to improve accuracy and address real-world issues in AI applications for construction.