Top Computer Vision Jobs in Seattle, WA

Lead research and development of perception algorithms for Physical AI, including detection, segmentation, tracking, localization, sensor fusion, and scene understanding. Define rigorous datasets, experiments, metrics, and evaluation pipelines, then optimize models for real-time deployment across Arm CPUs and AI accelerators. Collaborate with robotics, controls, hardware, product, and customer teams; mentor technical staff; guide on-device perception roadmaps; and contribute through publications, patents, and open-source work.

Lead design, training, fine-tuning, evaluation, and deployment of vision, vision-language, and vision-language-action models for autonomous systems. Build scalable data pipelines, SFT workflows, and evaluation frameworks. Optimize models for embedded hardware (ONNX, TensorRT), collaborate cross-functionally, translate research to production, and mentor engineers.

As a Senior Machine Learning Engineer, you will design and deploy computer vision models, collaborate with teams, and mentor juniors while ensuring high-performance solutions.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

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.

Research and develop computer vision, sensor fusion, pose estimation, and machine learning solutions for imaging and embedded applications. Build prototypes and end-to-end pipelines in Python and C++, optimize models for real-time edge deployment, and implement MLOps practices for monitoring, versioning, and CI/CD. Evaluate novel algorithms against current research, develop preprocessing and augmentation workflows, and transition validated concepts toward production.

Design and own end-to-end computer vision and machine learning pipelines for deployed robotic fleets, covering data collection, training, sensor fusion, evaluation, edge deployment, and telemetry. Develop data engines with auto-labeling, synthetic data, and observability loops; integrate perception with navigation and planning through ROS 2. Evaluate emerging AI methods against latency, cost, determinism, and safety constraints. Mentor engineers, collaborate across hardware and software teams, and occasionally advise clients.

The Senior Research Scientist will develop algorithms for video content analysis, utilizing machine learning and computer vision to create robust prediction models while collaborating with software teams.

Develop production-grade perception and machine learning systems for autonomous driving, including object tracking, sensor fusion, action and attribute recognition, and semantic understanding. Research deep learning models, analyze failures, optimize large-scale training pipelines, and deploy real-time solutions on Aurora’s Driver platform. The role also involves architectural strategy, software lifecycle ownership, and cross-functional collaboration across simulated and on-road testing.

Conduct research and engineering to improve Claude's visual and spatial capabilities across pretraining, SL, and RL. Run experiments on architectures and data strategies, build agentic tools and evaluation benchmarks, develop data ingestion pipelines, and partner with product to deploy vision improvements for real-world API use cases.