Top Deep Learning Jobs in San Francisco Bay Area, CA

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.

The Staff Software Engineer will optimize deep learning networks for autonomous vehicle systems, focusing on performance analysis, software architecture, and collaboration with cross-functional teams.

Drive applied research and development of deep learning and LLM-based solutions for recommendation systems and real-time bidding. Prototype, train, fine-tune, and deploy production-grade ML models, optimize scalability and performance, and collaborate with engineering, product, and platform teams to deliver large-scale AdTech ML systems.

The role involves designing and implementing deep learning solutions for object detection, tracking, and segmentation on drones, while prototyping new approaches and optimizing models for performance on embedded hardware.

Lead development of spatial deep learning models (diffusion and 3D generative) from data curation through training, evaluation, and production deployment; create scalable training datasets from LiDAR, images, videos, and floorplans; combine geometry algorithms with learned models and integrate outputs into an interactive 3D product stack.

The role involves analyzing deep learning networks, optimizing compiler algorithms, and collaborating across teams to enhance deep learning software performance and usability.

The Senior DL Algorithms Engineer will optimize deep learning models for performance, analyze bottlenecks, and contribute to open-source frameworks while collaborating across teams for AI model development.

The role involves optimizing GPU performance for neural reconstruction workflows, analyzing bottlenecks, and collaborating with engineers to enhance training and rendering systems.

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

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.