Top Computer Vision Jobs in Los Angeles, CA

Leads the technical direction of an autonomous vehicle perception stack. Responsibilities include architecting scalable perception systems, developing scene detection and runtime activation, improving robustness against sensor degradation and adverse weather, integrating multimodal models, managing ML and data pipelines, monitoring field performance, leading testing and feature delivery, and mentoring engineers. Requires strong robotics, computer vision, machine learning, real-time systems, software architecture, and team leadership experience.

Design and train foundation vision-language models that read entire CT studies end-to-end. Build custom PyTorch architectures and distributed training pipelines, run large-scale experiments, evaluate models for clinical safety, and move models through FDA submission to hospital deployment.

As a Computer Vision Applied Research Scientist, you'll design novel architectures for understanding construction drawings, conduct end-to-end experiments, and transition models to production. You'll collaborate with others to improve accuracy and address real-world issues in AI applications for construction.

Lead design and delivery of an egocentric video data product: capture rigs, edge processing, dataset pipelines, VLM-assisted annotation, and production-ready perception models (detection, tracking, segmentation, depth/3D reconstruction, and 6DoF pose estimation). Operate and scale warehouse capture programs and own model training, evaluation, optimization, and deployment.

Lead and execute perception R&D and engineering for factory robots: define multi-year roadmap, architect perception pipelines (detection, segmentation, 6-DoF pose, scene understanding), drive sensor fusion and calibration, establish data/model workflows and deployment practices, integrate with planning/control, hire and mentor engineers, and evaluate research for production adoption.

Lead design and implementation of computer vision systems for mapping, localization, image analysis, and 3D reconstruction on construction site data. Develop and optimize SLAM/SfM/VIO, deep learning perception models, and multi-modal approaches. Prototype, test, oversee datasets and collaborate with platform engineers to productionize scalable, robust solutions.

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.