Staff CV Applied Research Engineer, Edge AI

Lead end-to-end development of edge ML models for outdoor monitoring on resource-constrained embedded devices (e.g., outdoor cameras, doorbells); architect, train, and deploy transformer-based vision models and hybrid CNN-transformer backbones optimized for embedded inference; drive model efficiency through architecture decisions, quantization/pruning, distillation, and mixed precision to meet strict FPS/memory/power budgets; partner with embedded/firmware and platform teams to integrate models into production pipelines; set evaluation strategies for robustness in night, weather, and fast-motion conditions; mentor other ML engineers and raise engineering standards across experimentation, versioning, and deployment; required strong Python and PyTorch/TensorFlow skills and senior, staff-level leadership experience.