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Field AI

Robotics Autonomy Engineer-Planning and Control-Federal

Irvine, CA$70k–$300kfull timemidAdded 1 month ago

About this role

Field AI seeks a Robotics Autonomy Engineer to design and deploy motion planning and control algorithms for real-world robotic systems operating in complex environments. You'll develop robust navigation solutions, integrate planning with control layers, and validate algorithms from simulation through field deployment across wheeled, legged, and humanoid platforms.

What you'll do

  • Design and refine motion planning algorithms for challenging real-world scenarios with dynamic obstacles and complex terrain
  • Develop optimization-driven trajectory generation approaches for smooth and efficient robot navigation
  • Develop and tune control algorithms for precise trajectory tracking across different robotic systems
  • Build testing pipelines and benchmarking environments for algorithm validation from unit-level to full deployment
  • Analyze field telemetry data to diagnose issues and implement targeted performance improvements
  • Collaborate across autonomy layers to integrate perception, planning, and control systems

What they're looking for

  • Motion planning and trajectory generation
  • Control systems design and tuning
  • C++ and Python programming on Linux
  • ROS/ROS 2 robotics middleware
  • Motion planning libraries (OMPL, MoveIt, Nav2)
  • Robot sensor integration (LiDAR, cameras, IMUs, encoders)
  • Simulation and testing framework development
  • Optimization and numerical methods
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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 developing motion planning algorithms for real robots. What was the most challenging environment or obstacle type you had to handle, and how did you approach it?
  • Describe your experience with motion planning libraries like OMPL, MoveIt, or Nav2. How have you customized or extended these frameworks for specific robotic systems?
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