Top Computer Vision Jobs in Los Angeles, CA

Develop and productize autonomous-driving perception software. Responsibilities include full SDLC execution, writing and testing C++ and Python code, integrating perception components, creating verification plans, deploying deep-learning models to embedded platforms, debugging systems, and evaluating performance with real and synthetic data.

Perform image segmentation and object labeling for computer vision datasets using polygons, bounding boxes, and semantic segmentation. Follow annotation guidelines, complete quality checks, meet daily productivity targets, communicate unclear tasks to team leads, and suggest workflow improvements. Work remotely with datasets involving roads, vehicles, satellite imagery, blueprints, roofing, and related visual content.

Review and annotate robotic-system video footage by identifying objects, movements, events, visual features, and anomalies. Provide feedback to improve video datasets and annotation consistency, collaborate with AI teams on annotation protocols, document observations, participate in quality reviews, and recommend improvements to video capture and labeling strategies.

Perform object detection annotation on visual datasets using digital annotation tools. Label and review objects, apply project guidelines, correct inconsistencies, maintain data quality and confidentiality, and meet productivity standards. The role requires accuracy during repetitive tasks, independent remote work, collaboration with project teams, and a consistent 40-hour weekly schedule.

Own the end-to-end computer vision platform for fine-grained item identification and pricing. Responsibilities include designing and fine-tuning vision models, defining evaluation and confidence standards, building labeling feedback loops, prioritizing coverage versus accuracy, and deploying reliable, low-latency serving infrastructure at scale. The role also communicates model capabilities and trade-offs to technical and non-technical stakeholders.

Annotate construction drawings using bounding boxes, polygons, and segmentation masks for computer vision models. Maintain consistent, accurate labels, clean datasets, identify ambiguous cases, and help improve annotation guidelines. Meet weekly quality and throughput targets while communicating data issues to ML engineers. The role requires focused repetitive work, precise instruction-following, independent remote work, and clear written English. It is a remote, 3–6 month contract with potential full-time conversion.

Designs and demonstrates computer vision solutions for customer opportunities, including model selection, data strategy, evaluation, edge deployment, and business-case development. The role leads discovery, high-level architecture, proofs of concept, demos, partner engagement, and technical enablement. It requires hands-on expertise across classical computer vision, deep learning, video intelligence, vision language models, model lifecycle management, and edge optimization, plus strong customer-facing and commercial communication skills.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.

Own customer-facing computer vision deployments from proof of concept through production adoption and maintenance. Build data pipelines, edge-device systems, models, and infrastructure using Python, Docker, Kubernetes, Linux, NVIDIA Jetson, and MLOps. Troubleshoot physical-environment challenges such as lighting, calibration, model drift, networking, and hardware constraints. Work onsite with customers, communicate with technical and executive stakeholders, document architectures, and relay field insights to Product and Engineering. The role requires 40%–50% travel.

Senior product engineer owning end-to-end product surfaces for a computer vision platform. Ship features from idea to production, prioritize roadmap, design UX details, integrate APIs, improve annotation and model-assisted labeling, and collaborate with engineering and users to drive product impact.