Top Computer Vision Jobs in NYC, NY

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.

As an AI Vision Solution Architect, you will architect AI-powered solutions, manage projects, collaborate with teams, and build customer relationships to enhance AI vision products.

The Computer Vision AI Engineer will develop and maintain computer vision algorithms for various systems, optimizing performance and analytics, specifically involving robots and autonomous hardware.

Designs and demonstrates computer vision solutions for customer opportunities, including model selection, data strategy, evaluation, edge deployment, and business-case development. The role leads discovery, high-level architecture, proofs of concept, demos, partner engagement, and technical enablement. It requires hands-on expertise across classical computer vision, deep learning, video intelligence, vision language models, model lifecycle management, and edge optimization, plus strong customer-facing and commercial communication skills.

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.

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.

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.