8-foot expandable drone platform for agriculture, disaster response, and defense
WingXpand builds a compact, deployable aerial platform targeting agriculture, public safety, and defense markets. The stack reveals a hardware-first, AI-enabled operation: embedded C/C++ and PX4 flight control paired with Python, TensorFlow Lite, and YOLO for onboard ML inference, plus ROS for robotics integration. Active pain points around prototype-to-production scaling, manufacturing workflow consistency, and autonomous systems execution—combined with hiring skewed toward engineering (13 roles) and manufacturing (3)—indicate they're at the critical juncture of moving from development builds toward field-hardened production units.
WingXpand manufactures an 8-foot expandable drone system designed to pack into a backpack and deploy for agricultural yield optimization, disaster response coordination, and defense operations. The company operates from St. Louis with 11–50 employees and is actively scaling engineering and manufacturing capacity. Current work spans battery management, ground control software, power system design, environmental testing of aircraft electronics, and field operations support. The hiring mix—predominantly engineering with growing manufacturing support—reflects the technical depth required for autonomous flight systems and the operational challenges of moving prototypes into reliable production.
C, C++, and Python. They use C/C++ for embedded flight control (PX4 platform), Python for AI/ML workloads, and ROS (Robot Operating System) for robotics integration.
TensorFlow Lite, YOLO, Google Colaboratory, and ROS. These enable onboard inference and autonomous navigation capabilities.
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