AI-powered shelf audit and inventory platform for retail
Simbe builds an autonomous shelf-auditing system combining robotics, computer vision, and sensor data to detect out-of-stock items, pricing errors, and inventory gaps. The tech stack is heavily robotics-focused (ROS, ROS 2, C++, Nvidia Jetson) with cloud infrastructure (GCP, AWS, Azure), reflecting a hardware-software integration challenge. Current hiring is engineering-heavy and accelerating, with active projects around ROS integration, autonomous navigation, and CI/CD infrastructure — all pointing toward production scaling and system reliability as the critical bottleneck.
Simbe operates an autonomous store-audit platform deployed in retail environments globally. The system uses robots equipped with computer vision and RFID sensors to monitor shelf conditions, detect pricing discrepancies, and track inventory in real time. The company sells to mid-market and enterprise retailers; deployment is typically embedded within existing POS and inventory systems. Simbe is privately held, headquartered in South San Francisco, and operates with a 51–200-person team split primarily between engineering and operations roles.
Robotics-focused: ROS, ROS 2, C++, Python. Infrastructure: GCP, AWS, Azure. Data and DevOps: Docker, Redis, Git. Compute: Nvidia Jetson for edge processing.
Core projects: autonomous robot stack and navigation, ROS integration, tally software platform. Infrastructure: DevOps, CI/CD pipeline development, production readiness for tier-1 B2B customers.
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