Top Computer Vision Jobs in Denver & Boulder, CO

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.

Develop hybrid computer-vision and deep-learning perception systems for autonomous spacecraft. Responsibilities include multi-object tracking, neural-network detection and classification, coordinate transformations, image processing, synthetic-data generation, edge deployment, and software-, processor-, and hardware-in-the-loop validation. The role involves implementing Kalman filters, training PyTorch models, writing C++ flight code, and optimizing algorithms for space-qualified processors. This is a three-month temporary engagement with potential conversion to regular employment.

The Senior Staff Machine Learning Scientist will lead research initiatives in computer vision and multimodal understanding, develop novel models, and translate research into practical applications while mentoring team members.

Develop, train, and optimize computer vision and ML models (particularly video-based safety) using petabyte-scale camera and sensor data. Deploy and optimize models for backend and edge inference, collaborate with cross-functional teams, and translate customer needs into scalable ML/CV solutions.

Develop, research, optimize, and deploy computer vision AI/ML algorithms for remote sensing, including automatic target recognition and multimodal EO/SAR data fusion. Responsibilities include processing large datasets, training supervised and unsupervised deep learning models, deploying solutions to cloud environments, reviewing research, documenting capabilities, and potentially leading junior engineers. The role requires an active TS/SCI clearance and supports defense and commercial applications.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.

Lead pre-sales solutioning for Physical AI, primarily computer vision, video intelligence, spatial intelligence, and vision language models, with supporting robotics expertise. Engage customers, qualify opportunities, design solutions, build business cases, and develop proofs of concept and demonstrations. Enable sales teams, collaborate with technology partners, create technical content, and contribute to developing WWT’s Physical AI practice and repeatable offerings.

Develops, trains, tests, deploys, and maintains production computer vision and machine learning models for mission-critical applications. Responsibilities include deep learning, transformer-based vision models, sensor fusion, target tracking, GPU optimization, embedded systems, and real-time missile defense software. The engineer collaborates with multidisciplinary space, military, intelligence, engineering, and AI teams to design and implement robust operational capabilities.

Own customer-facing computer vision deployments from proof of concept through production adoption and maintenance. Build data pipelines, edge-device systems, models, and infrastructure using Python, Docker, Kubernetes, Linux, NVIDIA Jetson, and MLOps. Troubleshoot physical-environment challenges such as lighting, calibration, model drift, networking, and hardware constraints. Work onsite with customers, communicate with technical and executive stakeholders, document architectures, and relay field insights to Product and Engineering. The role requires 40%–50% travel.

Lead the development and deployment of production-ready computer vision and deep learning systems for medical imaging. Responsibilities include model development, end-to-end ML pipelines, validation, data strategy, clinical readiness, explainability, regulatory support, cross-functional collaboration, and technical mentorship. The role requires translating research into reliable, clinically validated AI solutions for real-world healthcare environments.