Leads robotics automation strategy and hands-on software architecture for manipulators and automated forklifts, spanning controls, autonomy, cloud systems, team leadership, and production deployment.
About this role
This engineer develops perception for TX GHOST, a robot performing autonomous pick-and-place work in Japanese convenience stores. RGB, depth and other data from deployed robots are used to improve performance in changing retail environments.
Work covers deep-learning and computer-vision algorithms, quantitative analysis, safe deployment, and optimization for constrained hardware. Tasks include detection, segmentation, keypoints, 3D pose or reconstruction, augmentation, and acceleration with TensorRT or TorchScript. Core tools are Python, PyTorch, OpenCV, NumPy and SciKit.
Candidates need at least three years developing perception systems or equivalent academic research, strong debugging and monitoring, low-level vision and ML depth, experience implementing modern methods from scratch, RGB plus depth or LiDAR work, customization of deep-learning applications, and speed or latency optimization.
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