Top Computer Vision Jobs in Chicago, IL

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 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.

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.

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.

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.

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.