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.

Develop deep learning and 3D computer vision models for state estimation, visual navigation, localization, depth, correspondence, and pose estimation. Own data preparation, training, evaluation, benchmarking, and reproducible workflows. Integrate learned measurements into VIO and terrain-relative navigation systems, optimize models for onboard compute, and collaborate with deployment, software, systems, and flight-test teams to deliver production-ready Hivemind SDK components.

Conduct research engineering for a probabilistic foundation model spanning temporal, tabular, and mixed-modality data. Design model architectures, encoders, attention mechanisms, distributional output heads, and decoding strategies; implement and train scalable PyTorch models; run controlled experiments, ablations, and performance profiling; diagnose training behavior; and produce reproducible, production-quality code. Candidates with stochastic modeling expertise may also develop synthetic data generators and simulate richer stochastic dynamics.

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

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.

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.

As a deep learning infrastructure engineer, you will build and optimize systems for deep learning, focusing on computer vision tasks and MLOps workflows.

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 ML Research Engineer will implement and deploy deep-learning models for perception AI, manage data pipelines, and design user interfaces.

The role involves designing and training deep learning models for genomic analysis, collaborating with scientists, and publishing research findings.