Wing operates a drone-delivery fleet designed to move packages directly from businesses to homes, competing with traditional last-mile logistics. The stack reveals a hardware-software hybrid: embedded systems (C++, RTOS, MATLAB, SolidWorks for drone design and control) paired with ML infrastructure (PyTorch, TensorFlow, JAX) to power autonomous flight and real-time decision-making. Active projects around deploying ML models on drones and applying computer-vision foundation models suggest the company is shifting from hand-coded flight logic toward learned behaviors — a typical maturation pattern in autonomous systems.
Notable leadership hires: Chief Pilot
Wing delivers small packages via autonomous drones directly to homes and businesses, positioning itself as an alternative to traditional delivery logistics. The company operates a managed fleet of lightweight, automated delivery aircraft integrated with partner business systems. The technology stack spans embedded systems (C++, RTOS, Embedded Linux), manufacturing tools (SolidWorks, MES, PLM), and ML infrastructure (TensorFlow, PyTorch, JAX), reflecting the complexity of designing, building, and operating hardware at scale. Current work includes fleet-technology improvements, partner onboarding, and a consumer-facing mobile app for order tracking and delivery coordination. Based in Palo Alto with 51–200 employees, the company is actively scaling across engineering, operations, and data teams.
Wing's stack includes C++, C/C++, RTOS, and Embedded Linux for drone control; MATLAB and SolidWorks for design and simulation; PyTorch, TensorFlow, and JAX for machine learning; and AWS and GCP for cloud infrastructure. ServiceNow and Workday handle operations and HR.
Active pain points include hardware reliability, fleet availability, and scaling fleet operations. The company also tracks inefficient delivery methods, cost reduction, and core ERP functionality as internal priorities.
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Wing's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.