AI and laser-based weed control robots for large-scale specialty crops
Carbon Robotics builds LaserWeeder machines that use computer vision and deep learning to distinguish crops from weeds and eliminate weeds with lasers. Its stack blends embedded firmware (Zephyr, FreeRTOS, UART, I2C), robotics tooling (ROS, LiDAR, GNSS, PLCs) and PyTorch, signaling a full hardware-to-model engineering org. Accelerating, engineering-led hiring alongside sales pipeline projects for a second product line, Carbon Autonomy, points to expansion beyond weeding.
Carbon Robotics is a privately held agricultural robotics company founded in 2018 and headquartered in Seattle, Washington, with 51–200 employees. It designs AI-driven laser weeding machines for large specialty crop growers. Growers in North America, Europe and Australia run its machines. Design happens in Seattle, while manufacturing takes place at a company facility in Richland, Washington. The company is hiring in the United States, the Netherlands, Australia and Peru across engineering, manufacturing, sales, marketing and support.
Carbon Robotics uses C++, Python, PyTorch, ROS, Zephyr and FreeRTOS for robotics and embedded work, plus Docker, Kubernetes, Terraform, TypeScript, React, Flutter, SolidWorks, Altium Designer, HubSpot and Salesforce.
Yes. Engineering is the largest hiring area, with demand also in manufacturing and sales, and hiring velocity is accelerating across the United States, Netherlands, Australia and Peru.
Current work includes the LaserWeeder deep learning system, electrical power system design, sensor and actuator integration for Carbon Autonomy, and building sales pipelines for both products.
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Carbon Robotics'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.