Top Computer Vision Jobs in Chicago, IL

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

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.

Develop learning-enabled imaging algorithms for 3D reconstruction and real-time adaptive acquisition of noisy synchrotron datasets; implement multimodal alignment and fusion of structural, chemical, and biological signals; collaborate with scientists across computational science, physics, biology, and environmental chemistry to guide experiments and analyze data.

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.

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.

Own the end-to-end computer vision platform for fine-grained item identification and pricing. Responsibilities include designing and fine-tuning vision models, defining evaluation and confidence standards, building labeling feedback loops, prioritizing coverage versus accuracy, and deploying reliable, low-latency serving infrastructure at scale. The role also communicates model capabilities and trade-offs to technical and non-technical stakeholders.

Annotate construction drawings using bounding boxes, polygons, and segmentation masks for computer vision models. Maintain consistent, accurate labels, clean datasets, identify ambiguous cases, and help improve annotation guidelines. Meet weekly quality and throughput targets while communicating data issues to ML engineers. The role requires focused repetitive work, precise instruction-following, independent remote work, and clear written English. It is a remote, 3–6 month contract with potential full-time conversion.