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Mid-level, metro location, and broad skills create moderate candidate competition.
Highly domain-specific robotics and embedded autonomy skills limit cross-industry transferability.
Explicit years, advanced degree preference, and specialized ROS2/Jetson/CUDA skills raise strictness.
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Design, build, and integrate perception, mapping, localization, and planning modules for autonomous robotic systems operating in complex, GPS-denied environments.
Develop and optimize multi-sensor fusion algorithms (camera, LiDAR, radar, IMU, GPS) and real-time autonomy pipelines suitable for embedded platforms like NVIDIA Jetson.
Lead sensor calibration, synchronization, system integration, field testing, and debugging under real-world and degraded conditions.
3–4 years of professional experience in Robotics, Computer Vision, or Autonomous Systems.
Master’s or PhD degree in Robotics, Autonomous Systems, Computer Vision, AI/ML, Computer Science, or related field.
Proficient in C++ and Python programming; hands-on experience with ROS 2 and Linux environments.
Experience with sensor fusion, localization, mapping, motion planning and embedded AI platforms such as NVIDIA Jetson and CUDA.
Engineer with substantial hands-on expertise in end-to-end autonomy systems deployed in real-world, unstructured environments.
Comfortable working across perception, multi-sensor fusion, and planning pipelines with strong system integration and debugging capabilities.
Experience optimizing algorithms to run on embedded platforms and familiarity with robotics simulation tools (e.g., Gazebo, CARLA, Isaac Sim) is a plus.