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Field AI

Robotics AI Engineer - Sensor Calibration

Irvine, CAfull timemidAdded today

About this role

Field AI is hiring a Robotics AI Engineer to develop and optimize sensor calibration systems for autonomous robots operating in challenging environments. You'll work on multi-sensor calibration pipelines (cameras, LiDAR, IMUs) using C++ and Python, collaborating with a specialized team to enable reliable perception across construction, security, mining, and manufacturing applications.

What you'll do

  • Develop sensor calibration workflows for multiple modalities including intrinsic, extrinsic, and temporal calibration
  • Design scalable systems to manage and process large-scale sensor datasets for calibration
  • Implement and optimize calibration algorithms in C++ and Python with clean, maintainable code
  • Integrate emerging technologies to enhance sensor calibration workflows
  • Collaborate cross-functionally to meet project documentation and deliverable requirements
  • Support downstream localization, mapping, and autonomy systems through robust calibration

What they're looking for

  • C++ and Python programming
  • 3D geometry and computer vision
  • Sensor calibration techniques (camera intrinsic, LiDAR-Camera, multi-sensor extrinsic)
  • Sensor data processing tools (PCL, Open3D, OpenCV, Kalibr)
  • Robot Operating System (ROS) and Ubuntu development
  • Backend optimization libraries (Ceres, GTSAM, g2o)
  • Problem-solving and independent work management
  • Technical communication and documentation
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Field AI

Field AI develops embodied AI and autonomous robotics systems for real-world deployment in industrial environments like oil & gas and mining. The company is hiring software engineers to build web-based systems, perception and validation pipelines, test infrastructure, ROS-based robotic software, and customer-facing products that integrate AI with field-deployed hardware.

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Likely interview questions

  • Walk us through your experience with multi-sensor calibration—which sensor combinations have you worked with and what challenges did you face?
  • How have you approached designing scalable systems for processing large sensor datasets, and what tools or frameworks did you use?
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