Top Deep Learning Jobs in San Francisco Bay Area, CA

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.

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.

Lead and grow an engineering team building GPU‑accelerated deep learning inference frameworks. Drive roadmap, collaborate with compiler/libraries/research teams, optimize LLM and multimodal model performance, and oversee multi‑GPU communications and production deployment of inference software.

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.

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