Moe Sani builds machine learning solutions for robotics and embedded systems natively on the edge. As an Edge AI Solutions Architect at Edge Impulse, he focuses on turning complex AI models into practical applications that run without cloud dependency.
Before joining Edge Impulse, he directed the research and deployment of the first machine learning-based navigation system for Dyson robots. His recent work explores physical AI and autonomous systems, specifically multi-model edge architecture using ROS 2 and local language models to bridge the gap between software demos and working hardware.
