Top Computer Vision Jobs in Seattle, WA

Develop and scale state-of-the-art computer vision pipelines for aerial and oblique imagery. Train and improve segmentation, detection, and generative models, run large-scale PyTorch experiments, maintain modular ML infrastructure, and collaborate on deployment and inference optimizations while reproducing top-tier research.

Develop and extend a C++ annotation and reporting tool for computer vision and robotics. Implement tracking, detection, deeper annotations, quality measurement, automated reporting, and automated tests. Work with OpenCV, SQL/reporting services, HTML/JavaScript/CSS, MSBuild, and Linux while collaborating with applied scientists and engineers.

Design and implement high-fidelity spatial mapping and real-time object recognition pipelines; integrate vision outputs with robotic manipulation and navigation systems; and train and fine-tune Vision-Language-Action models to improve robotic autonomy and perception.

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.

Builds and deploys physical AI and computer vision systems for enterprise customers. Responsibilities include curating video data, training and evaluating models, integrating cameras through RTSP and ONVIF, deploying inference to edge or cloud hardware, building alerts and dashboards, managing retraining loops, and supporting pilots at customer facilities. The role also creates deployment documentation, demo environments, simulations, digital twins, and robotics integrations.

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.

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.

Build end-to-end front-end tooling and active-learning agents for a spatial CV platform. Train and deploy lightweight CV models, create pre-labeling and annotation systems, design planogram and spatial interfaces, and iterate rapidly to deliver intuitive visual workflows for researchers and non-technical users.