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Field AI

Robotics Autonomy Engineer-Planning and Control - Federal

Irvine, CA$100k–$200kfull timemidAdded 4 days 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, unpredictable environments. You'll develop robust navigation solutions, integrate perception-planning-control stacks, 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 real-world scenarios including dynamic obstacles and narrow passages
  • Develop and tune control algorithms for precise trajectory tracking across diverse robotic systems
  • Build testing pipelines from unit validation to full robot deployment with simulation and real-world benchmarking
  • Analyze field telemetry to diagnose issues and deliver targeted performance improvements
  • Collaborate across autonomy layers to ensure seamless perception-planning-control coordination
  • Ensure scalability and reusability of planning approaches across different deployment contexts

What they're looking for

  • Motion planning and trajectory generation
  • Control systems design and implementation
  • C++ and Python programming on Linux
  • ROS/ROS 2 robotics middleware
  • Motion planning libraries (OMPL, MoveIt, Nav2)
  • Robot sensor integration (LiDAR, depth cameras, IMUs, encoders)
  • Algorithm validation and testing
  • 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 a motion planning challenge you solved for a specific robotic platform and how you validated it in real-world conditions.
  • How do you approach tuning control algorithms when there's a mismatch between simulation and actual robot behavior?
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