Top Computer Vision Jobs in Seattle, WA

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.

The Senior Staff Machine Learning Scientist will lead research initiatives in computer vision and multimodal understanding, develop novel models, and translate research into practical applications while mentoring team members.

Develop, train, and optimize computer vision and ML models (particularly video-based safety) using petabyte-scale camera and sensor data. Deploy and optimize models for backend and edge inference, collaborate with cross-functional teams, and translate customer needs into scalable ML/CV solutions.

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.

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 deploy in-house vision-language models for smart home video understanding: track research, train and evaluate multimodal VLMs on large-scale video data, design evaluation pipelines, build prototypes, inform product direction, and publish or open-source research.

Lead end-to-end research in computer vision, machine learning, and generative AI to build scalable, privacy-preserving multimodal perception models. Train and optimize models for cloud and resource-constrained devices, validate performance in real-world settings, publish and patent findings, and mentor junior scientists while collaborating with product and engineering teams.

Lead end-to-end computer vision and IIoT engineering projects to design, prototype, and deploy ML/CV solutions for material handling and operator workflows. Drive system design, vendor collaboration, simulation, documentation, and multi-project management to deliver scalable operational improvements.