Top Computer Vision Jobs in San Francisco Bay Area, CA

Own the architecture and technical direction of a production perception system for home security hardware. Build robust computer vision and machine learning pipelines for multi-camera tracking, sensor fusion, inference, and edge deployment. Improve accuracy, latency, reliability, observability, and graceful failure handling across challenging environmental conditions. Lead evaluation infrastructure, field-data feedback loops, system interfaces, technical reviews, and cross-functional collaboration with hardware, cloud, and product teams.

Research and implement neural rendering methods such as NeRF and 3D Gaussian splatting for autonomous construction simulation. Train models on real-world fleet data, integrate rendered outputs into simulation pipelines, develop visual fidelity and autonomy-transfer metrics, and address challenges including dynamic objects, deformable terrain, dust, and harsh lighting. Document findings and recommend where neural rendering should replace traditional simulation assets.

Leads Zoox’s perception data organization and data flywheel, connecting fleet signals, data discovery, curation, annotation, model training, evaluation, and performance measurement. Manages a multidisciplinary team of data science, data engineering, and labeling professionals. Drives automated annotation, intelligent mining of rare and safety-critical scenarios, log selection and storage, and rigorous tradeoffs among data quality, cost, speed, and scale. Partners with machine learning, metrics, evaluation, and infrastructure teams to improve autonomous-driving models.

Computer Vision Intern developing detection, segmentation, and classification models for food and material recognition. Responsibilities include curating and QAing image/video datasets, building reproducible training and experiment-tracking pipelines, evaluating models, documenting findings, and collaborating with engineering and data teams to deploy computer vision systems to production devices.

Hands-on machine vision applications engineer responsible for lab testing, camera and optical setup, proof-of-concept builds, technical reporting, technology evaluation, customer-site troubleshooting, and deployment support. The role also includes image collection, labeling, model training, model evaluation, camera calibration, hardware evaluation, and building internal test resources. Candidates need machine vision fundamentals, AI vision familiarity, troubleshooting ability, and comfort working with hardware, industrial automation, and manufacturing customers.

Build and improve a scan-to-BIM pipeline that converts point-cloud capture data into accurate, structured 3D BIM models. Responsibilities include model creation, mesh generation, data structuring, IFC and 3D-tool usage, and applying computer vision to automate 3D reconstruction. The role requires construction knowledge and hands-on experience with detailed 3D projects, with occasional quarterly onsite gatherings in San Francisco.

Research and productionize 3D computer vision and spatial intelligence pipelines for simulation and training applications. Responsibilities include 3D reconstruction, segmentation, feature embeddings, multimodal model integration, scene-graph generation, GPU-accelerated training and inference, synthetic-data workflows, performance profiling, and deployment across cloud and edge environments. The role collaborates with graphics and product engineers to convert advanced research into reliable real-time capabilities.

Develop computer-vision methods for detecting and tracking insects in behavioral-assay videos. Build labeled datasets, evaluation metrics, video-processing pipelines, confidence estimates, visual overlays, and quality-control tools. Derive interpretable behavioral measurements and collaborate with entomologists, laboratory teams, and software engineers to improve imaging standards, annotations, and scientific reliability.

Conduct research and engineering on multimodal AI, focusing on visual perception, vision-language interaction, model architectures, large-scale datasets, evaluation benchmarks, and distributed training. The role involves designing experiments, analyzing model performance, collaborating across research and product teams, publishing findings, and contributing high-performance code to frontier multimodal systems.

Technical leader for CV User Experience building end-to-end mobile and backend systems. Architect and scale TrueMotion-driven features, improve observability and performance, mentor engineers, lead architecture, and deliver high-quality, performant mobile experiences integrating computer vision and generative AI tooling.