Top Computer Vision Jobs in NYC, NY

Pre-sales Technical Solutions Architect focused on Physical AI: translates customer business problems into computer vision and robotics solutions, scopes and architects prototypes and POCs, enables sales teams, and develops practice content. Leads solution design across classical and modern vision toolkits, video and spatial intelligence, partners with ecosystem vendors, and builds business cases for deployments.

Pre-sales Technical Solutions Architect for Physical AI focusing on computer vision, video and spatial intelligence, and robotics. Engage customers and sales teams to qualify opportunities, design solutions, run proofs-of-concept, build business cases, collaborate with partners, and create technical content to develop the Physical AI practice.

Develop novel machine learning methods to enhance computer vision solutions, identify areas for innovation, and translate research into scalable production systems.

Join an R&D team to develop image-processing and computer-vision algorithms for 2D/3D scene segmentation, design experiments to detect long-tail model failures, and explore deep learning models and active learning methods to improve autonomous robotic vision systems.

The Machine Learning Engineer will develop editable visuals combining computer vision and machine learning, enabling chat-driven figure editing and leveraging biological understanding to produce accurate visuals.

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 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.

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.