Top Deep Learning Jobs in Los Angeles, CA

Develop and productionize deep learning models for computer vision and NLP on medical imaging products. Design and run experiments, document results, collaborate on data labeling, write deployment/docker code, optimize inference speed, and conduct DL methodology research.

The role involves researching, developing, and deploying deep learning models, optimizing machine learning lifecycles, and collaborating with teams to enhance manufacturing applications.

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.

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.

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.

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.

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.