Top Computer Vision Jobs in Chicago, IL

Lead and execute the production photogrammetry pipeline: design, build, train, and deploy 3D reconstruction systems at scale. Drive technical direction, optimize for speed and robustness, own observability and CI/CD, mentor engineers, integrate foundation 3D models and agentic AI tooling, and translate strategic goals into shipped work.

Build end-to-end front-end tooling and active-learning agents for a spatial CV platform. Train and deploy lightweight CV models, create pre-labeling and annotation systems, design planogram and spatial interfaces, and iterate rapidly to deliver intuitive visual workflows for researchers and non-technical users.

Design, train, and deploy custom object-detection models for retail; fine-tune and integrate vision-language models; optimize models for edge devices; build dataset and annotation pipelines; prototype research ideas; and provide technical leadership and mentoring for CV infrastructure and production ML systems.

Design, train, and deploy production computer vision and vision-language models for retail product recognition. Optimize models for edge devices, build data pipelines and annotation workflows, fine-tune open-source VLMs, develop VLA pipelines, mentor engineers, and drive CV infrastructure and MLOps best practices.

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).

The Computer Vision Engineer will develop and improve sports video models, conduct experiments, and collaborate with data teams to productionize models, focusing on player detection and tracking.

The Computer Vision Researcher will develop and deploy advanced computer vision models, ensuring full lifecycle management, collaborating with teams, and driving research initiatives for player tracking.

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.

Lead design and productionization of egocentric and warehouse perception systems: data capture, camera rigs, calibration, multi-view 3D and pose estimation, automated labeling pipelines, model training/optimization, deployment, and establishing dataset quality standards for embodied AI.

The Director of Computer Vision will lead technical vision, research, and execution in developing scalable AI-driven computer vision solutions, managing teams, and enhancing performance metrics.