Top Deep Learning Jobs in Seattle, WA

The Deep Learning Engineer will design and deploy deep learning systems for autonomous robots, optimize models, and mentor junior engineers.

Design and implement deep learning models (NLP and CV, including LLMs/transformers) to optimize ad relevance across Microsoft Ads, Bing, and Copilot. Deploy scalable production systems, run offline and online evaluations, and drive algorithmic improvements to enhance user and advertiser experiences.

Design, train, and deploy state-of-the-art deep learning models (transformers, CNNs) for end-to-end navigation. Build perception, prediction, and planning solutions, implement data pipelines, deploy and monitor models in production, detect drift, and troubleshoot integration and performance issues for real-world robotic systems.

The Staff Data Scientist will design and optimize deep learning models for fraud detection, mentor peers, and lead data science initiatives.

Design, optimize, and deploy deep learning models for customer behavior prediction. Perform feature engineering, implement research-informed architectures, scale training pipelines, collaborate with client teams, document findings, and uphold engineering best practices.

Lead design and training of large-scale transformer and hybrid foundation models across text and multimodal domains. Own architecture choices, diagnose large-scale training instabilities, optimize scaling tradeoffs (data, compute, model), design loss and alignment strategies, and execute distributed training (FSDP/ZeRO, tensor/pipeline parallelism) with mixed precision to build in-house foundation model expertise.

Design and implement novel neural network architectures for YOLO, run large-scale experiments and ablation studies, optimize models for accuracy and efficiency (quantization/pruning/distillation), build training and evaluation pipelines, and collaborate with R&D and engineering to productionize open-source models.

Conduct research and develop algorithms for real-time video processing and deepfake detection, translating machine learning models for production use.

Design, develop, and deploy state-of-the-art deep learning models for multimodal and satellite imagery; own ML architecture, infrastructure, model registries, and inference services; mentor the team, translate product vision into technical roadmaps, and integrate research into production-ready, scalable geospatial AI systems.

Lead the development of deep learning models for email data, optimize models, design evaluation frameworks, integrate products, and mentor junior researchers.