Top Computer Vision Jobs in San Francisco Bay Area, CA

Design and develop AI software for autonomous robotics, focusing on computer vision and VLA models, while collaborating across functions.

Design, train, and deploy large-scale computer vision and perception models (semantic segmentation, 4D geometry, preference models) for cloud and on-prem systems. Build data pipelines, annotation and evaluation tooling, validate models for field reliability, and ship production-grade features frequently. Senior roles lead architecture and experimentation; Staff roles own roadmaps for offboard perception models.

Design and prototype state-of-the-art visual encoders and multimodal representations; train billion-scale models on GPU clusters; build visual reasoning and retrieval tools; collaborate on text-to-image and VLM projects; mentor junior researchers; publish research and ship models to production.

Lead product development and operations for image and video AI benchmarking: build prompt libraries and evaluation frameworks, run human preference studies, manage generation pipelines, benchmark new models, engage industry partners, and support strategic marketing and community outreach to shape media-generation standards.

Develop and deploy applied ML and computer vision models to understand technical drawings and specifications. Work on multimodal reasoning across diagrams, text, and structured data, translate research into production within ~1 year, present prior research, and collaborate closely with founders to build customer-impacting solutions.

Design and evaluate imaging data representations and tokenization strategies for biological AI models. Coordinate across experimental, data science, engineering, and research teams to prioritize data acquisition, define quality criteria, and combine heterogeneous modalities into robust training frameworks that improve model performance and scientific interpretability.

Build, train, evaluate, and deploy production computer vision models (detection, segmentation, OCR, barcode decoding, visual search). Own dataset quality, tooling, model releases, inference exports, and monitoring. Collaborate cross-functionally to improve model performance, robustness, and deployment for retail robotics and edge environments.

Lead end-to-end design, prototyping, and productionization of computer vision and spatial AI systems (SLAM, mapping, localization, multi-agent tracking). Benchmark and integrate research or vendor solutions, optimize for edge devices, fuse camera data with other sensors, and mentor engineers while owning robust, production-ready pipelines and APIs.

Drive systems engineering lifecycle for sensing and localization in autonomous vehicles, ensuring robust solutions and leading integration across teams.

As a Machine Learning Engineer, design imaging algorithms, build acoustic simulations, and develop computer vision systems to enhance brain imaging technology.