Autonomous floor-cleaning robots with edge AI and fleet software
LionsBot builds autonomous floor-cleaning robots in Singapore, pairing an embedded stack (STM32, Jetson, ROS 2, C++) with edge inference tooling like TensorRT, OpenVINO and ONNX. Accelerating, engineering-heavy hiring targets multi-floor navigation, vision model deployment and a fleet dashboard, while adoption of Flutter and Dart points to a unified cross-device operator app. Pain points around fleet reliability and field failure analysis show a company maturing from launch to scaled operations.
LionsBot International is a privately held Singapore company, founded in 2018, that designs and manufactures autonomous smart floor-cleaning robots for commercial and industrial cleaning. Its robots are deployed in around 30 countries, supported by subsidiaries in the United States, India and the Netherlands. The company employs 51–200 people and recruits across the United States, Singapore, the Philippines, China, South Korea and the Netherlands. Alongside the hardware, it develops navigation, computer vision and cloud-connected fleet management software.
LionsBot uses ROS and ROS 2, C++, Python and PyTorch for robotics, TensorRT, OpenVINO and ONNX on Jetson for edge AI, STM32 for embedded control, React and TypeScript for web, and AWS, GCP and Azure for cloud.
Yes. Hiring is accelerating and weighted heavily toward engineering, mostly at mid and senior levels, with smaller openings in operations, support, data, design, finance and product.
LionsBot is hiring in the United States, Singapore, the Philippines, China, South Korea and the Netherlands, with headquarters in Singapore.
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LionsBot International'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.