Robotics Software Engineer (Navigation & Deployment)

About Kanaria Tech Kanaria Tech is a Tokyo-based frontier physical AI lab building the core AI capabilities for physically embodied intelligence. Our flagship technology is the Kanaria Robotic Model (KRM), an embodiment-agnostic, multimodal foundation model that gives robots socially aware and anticipatory navigation. We are starting with AMRs and will extend to other embodiments over time. About the Platform Team The Platform team turns KRM into a shippable system. It builds the full navigation stack around the model (SLAM with KRM serving as the local planner). It implements a logging system, scalable software, and operational-analytics dashboards, then deploys the complete software onto Jetson Orin. Its output product is called KRM NavBox. The Role We are looking for a robotics software engineer to build and improve KRM NavBox. You will integrate KRM into a production navigation stack, build the tooling and observability around it, and get it running reliably on edge hardware. What You'll Do Implement and maintain the navigation stack, integrating KRM as the local planner alongside SLAM (ROS 2, Nav2). Build logging, telemetry, and operational-analytics dashboards for deployed systems. Develop scalable, reliable production software and the tooling around model deployment. Deploy and optimize the complete software stack (including KRM quantization) on edge devices. Package and validate KRM NavBox for OEM partners. Work closely with the Physical AI team to productionize new KRM capabilities and with Robotics Operations team on field-readiness. What We're Looking For Strong software engineering skills in Python and/or C++. Experience with robotics middleware and navigation (ROS 2, Nav2, related NVIDIA tools). Familiarity with SLAM, localization, or motion planning. Experience deploying software to edge devices (Jetson, CUDA, TensorRT), including containerization (Docker). Experience building observability, logging, and operational-analytics dashboards and data pipelines. Nice to Have Model inference optimization and real-time systems. Experience shipping validated systems to industrial customers. Applied AI/ML knowledge; comfortable with fine-tuning, deploying, and using existing models.