Top Computer Vision Jobs in San Francisco Bay Area, CA

The role involves training and fine-tuning vision and language models, building evaluation and learning pipelines, and optimizing model performance.

Lead efforts in Object Detection and Tracking by architecting deep neural networks, adapting research papers into production solutions, and optimizing systems for real-time performance.

Design and implement high-fidelity spatial mapping and real-time object recognition pipelines. Integrate vision systems with robotic manipulation and navigation controls. Train and fine-tune Vision-Language-Action models and work with multimodal spatial AI frameworks and robotic hardware.

Build and own end-to-end product features for a CV/AI startup: interview customers weekly, design and implement full-stack solutions (React frontend, Python backend/infra), apply algorithms and system design, and drive product direction. Ship quickly, work closely with customers to translate domain knowledge, and iterate on blueprint-reading computer vision features for construction professionals.

Lead and grow a team of machine learning scientists to develop computer-vision methods for in‑vitro microscopy. Partner with lab and imaging software teams to co-develop assays, translate methods to computational biology, set roadmaps, participate in code and method reviews, and advance imaging QC and self-supervised/VLM-based approaches to drive therapeutic programs.

The role involves applying diffusion techniques and developing AI models to enhance 3D reconstruction pipelines for various industries.

Lead a team of Computer Vision and Machine Learning engineers, oversee architecture and development of models, manage training pipelines, and implement the latest research.

Design, implement, and productionize computer vision algorithms (detection, tracking, pose, activity, anomaly) for low-latency edge deployment. Build scalable ML data ingestion, retraining and redeployment pipelines, and partner cross-functionally to tailor ML solutions for customer deployments.

Annotate 3D LiDAR/point cloud and 2D camera imagery using bounding boxes, cuboids, polygons, and pixel-level segmentation. Identify and classify objects, static road features, and traffic landmarks per strict taxonomies to build HD maps. Collaborate with engineers, follow labeling guidelines, meet production benchmarks, and complete on-site training and a background investigation.

Design, train, and deploy real-time computer vision and multimodal perception models for person detection, tracking, pose/gesture/emotion recognition, and scene understanding. Build data collection/labeling pipelines, optimize models for edge inference, integrate perception with behavior and firmware, debug failures, and evolve perception architecture from prototype to deployed robot systems.