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Field AI

Robotics Autonomy Engineer - Perception

Irvine, CA$100k–$200kfull timemidAdded today

About this role

Field AI seeks a Robotics Autonomy Engineer to design and deploy perception systems for autonomous robots operating in harsh, unstructured environments. You'll own the full lifecycle of multi-sensor perception stacks—from algorithm development through field validation—integrating LiDAR, RADAR, and thermal sensors into production autonomy systems for federal programs.

What you'll do

  • Design, implement, and maintain robust perception systems for off-road autonomous robots in unstructured terrain
  • Develop localization and mapping algorithms (SLAM, VIO, LIO) that perform reliably in real-world field conditions
  • Integrate and fuse data from multiple sensor types (LiDAR, RADAR, cameras, thermal, inertial) into unified perception pipelines
  • Deploy perception software on physical robotic platforms and debug issues during on-robot testing and field operations
  • Create performance monitoring tools, regression tests, and metrics to ensure perception quality across deployments
  • Collaborate with autonomy, controls, and platform teams to ensure perception systems integrate into the full autonomy stack

What they're looking for

  • Robot perception, localization, and SLAM for real-world systems
  • C++ and Python robotics software development
  • ROS/ROS2 system architecture and development
  • Multi-sensor fusion and state estimation (VIO, LIO, sensor calibration)
  • Hardware debugging and sensor data troubleshooting
  • Field robotics deployment and validation
  • CI/CD pipelines and performance testing
  • Thermal/multispectral imaging perception (preferred)
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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

  • Describe a perception system you've deployed on a real robot in challenging outdoor conditions—what were the key failure modes and how did you address them?
  • Walk us through your approach to fusing data from heterogeneous sensors (e.g., LiDAR + thermal + RADAR) and how you validated the fusion results in the field.
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