AI-powered controller software for any robot arm, deployed cloud-native
Mujin US sells controller software that abstracts away robot-specific programming—any arm, any motion planner. The stack is modern and distributed (React/TypeScript frontend, Python/C++ backend, Kubernetes/Docker, PostgreSQL/Redis, multi-cloud AWS/GCP/Azure), but the pain-point list signals they're still solving foundational problems: product-market fit, hybrid cloud-on-prem deployments, and integrating disparate hardware. Hiring is engineering-heavy (25 of 39 roles) with accelerating velocity, suggesting they're building out platform capabilities faster than sales can move.
Mujin US develops motion-planning and control software for industrial robotic arms. The core product, the Mujin Controller, decouples application logic from hardware—users define a task and select any arm (Fanuc, Yaskawa, ABB) rather than coding robot-specific routines. The company augments the controller with vision systems and end-of-arm tooling to handle real-world variability in warehouse and logistics automation. Deployment spans cloud, edge, and on-premises environments, with a growing focus on remote diagnostics, over-the-air updates, and real-time telemetry dashboards. Founded in 2021 and headquartered in Suwanee, Georgia, the company serves manufacturing and logistics operators seeking to reduce programming friction and deploy robots faster.
React and TypeScript for frontends; Python and C++ for core robotics and motion planning; PostgreSQL and Redis for state; Kubernetes, Docker, and multi-cloud (AWS, GCP, Azure) for deployment. ROS, ROS 2, and OpenRAVE for robotics frameworks; Fanuc, Yaskawa, ABB for robot integrations.
Motion planning algorithms, CI/CD pipeline development, cloud-native diagnostics and analytics platforms, warehouse logistics deployments, remote maintenance via the controller, over-the-air updates, real-time operational dashboards, and mobile robotics integration with material handling equipment.
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Mujin US'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 →
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