Top Deep Learning Jobs in San Francisco Bay Area, CA

As a Staff AI Scientist, you will lead AI projects, design deep learning models using multimodal data, and ensure regulatory compliance in healthcare AI products.

Lead and grow a team building onboard deep learning behavior models for autonomous driving. Set technical strategy, guide large-scale model training and efficient inference, coordinate across perception, planning, simulation, validation, and infra teams, monitor KPIs, and manage resources to deliver safe, comfortable driving models.

As a Research Scientist, you will design AI technologies for motion planning in self-driving trucks, focusing on deep learning and seamless integration into production systems.

The role involves building and optimizing compression technology, designing neural models, and enhancing training systems for efficient and effective codec production.

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

The Research Scientist will explore improving deep learning models via training data interventions, collaborate with engineers and focus on customer-driven research outcomes.

Develop and research deep learning and vision algorithms for autonomous driving (detection, tracking, prediction, planning, SLAM). Lead end-to-end ML projects from data analysis through model evaluation, and collaborate across product, simulation, and algorithm teams to extend ML technologies into system components.

Design, implement, and train novel deep learning models at scale. Prototype architectures for tabular, relational, and graph data. Apply cutting-edge AI research, advise strategy, and partner with engineering, product, and design to productionize research into customer-facing features.

Support roadmap and requirements for cloud-hosted deep learning models; collaborate with ML, infrastructure, and product teams to execute features, track engineering velocity, improve models, and communicate product benefits while maintaining data handling and security practices.

Develop and optimize deep learning models to decode multimodal biosignals from custom sensors for real-time inference on edge hardware. Build neural architectures, fusion techniques, evaluation frameworks, and collaborate with hardware engineers and neuroscientists to deploy and benchmark models across users and datasets.