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Robotics Automation Perception Engineer (JP-Retail)

TELEXISTENCE

Responsibilities

  • Contribute to the development of an automation system using custom hardware by building sophisticated software for the perception system, incorporating deep learning, computer vision, and control algorithms.
  • Collaborate with other automation engineers to meet the priorities and goals set by the executive team.
  • Break down abstract goals into actionable tasks and manage your own timeline to achieve company objectives.
  • Analyze problems and data accurately and deploy robust and safe systems within the given timelines.
  • Collect data, analyze outcomes, and continuously improve algorithms with a focus on quantitative performance.

Preferred

  • Proficiency in modern C++ (C++11, C++14, or newer)
  • Experience with ROS

Knowledge of camera calibration and hand-eye calibration

  • Research experience in computer vision, AI, or robotics (e.g., publications at CVPR or ICRA)
  • Experience in synthetic dataset generation and domain randomization techniques using Blender, OpenGL, Isaac Sim, MuJoCo, etc.
  • Experience working with manipulators
  • A background in startups or fast-paced environments
  • Engineering experience in one or more of the following:
  • Motion planning and execution
  • Signal processing
  • Network communication (e.g., pub/sub, req/rep)

Requirements

  • 3+ years of experience developing perception systems or equivalent experience, including academic research.
  • Quick and accurate problem-solving skills.
  • Strong debugging and issue-monitoring capabilities.
  • Extensive experience in low-level computer vision, machine learning, and deep learning, including implementing state-of-the-art methods from scratch.
  • Proficiency in Python and commonly used libraries, such as PyTorch, OpenCV, NumPy, and SciKit.
  • Experience with libraries such as Open3D, Point Cloud Library, MMDetection, FastAI, etc., is not mandatory but is considered an advantage.
  • Experience working with RGB and depth sensors (or multi-sensor setups like RGB + LiDAR).
  • Familiarity with data augmentation techniques.
  • A proven track record in customizing deep learning applications, such as:
  • Object detection/segmentation
  • Keypoint detection

3D pose estimation and reconstruction

  • Experience with model performance optimization techniques (speed, latency, etc.), for example:
  • Network layer optimization
  • TensorRT / TorchScript