Senior Software Engineer, Navigation

Agility Robotics Agility Robotics · Robotics · Fremont +2 · Remote · Software Engineering

Senior Software Engineer role focused on designing, implementing, and deploying real-time motion planning and navigation systems for humanoid robots in complex logistics and manufacturing environments. This role is critical for scaling commercial deployments and improving autonomous locomotion behaviors.

What you'd actually do

  1. Design, implement, and deploy 3D motion planning algorithms for locomotion, with an emphasis on whole-body collision-aware motion execution in real-time.
  2. Own the core components of our navigation stack, specifically the local planning maps, terrain models, and grid map representations used for path and motion planning with collision avoidance.
  3. Advance our locomotion capabilities by developing and maintaining a 3D footstep path planner aiming to significantly reduce navigation cycle times.
  4. Define and implement the necessary navigation features and route planning algorithms to enable coordinated movement and resource sharing within multi-agent robot fleets.
  5. Drive the maturity of our release processes by designing, implementing, and maintaining robust regression testing pipelines for motion planning and navigation modules.

Skills

Required

  • 5+ years of professional experience in robotics
  • developing and deploying real-time navigation and motion planning systems for autonomous mobile platforms (humanoids, quadrupeds, autonomous vehicles)
  • Expertise in 3D/volumetric map representations (e.g., octomaps, voxel grids) for local path planning and collision avoidance
  • Deep technical understanding of locomotion-specific path and motion planning algorithms, including sampling-based planners (RRT/PRM), optimization-based methods (MPC/LQR), and hybrid A*
  • Expert proficiency in modern C++ (C++17/20)
  • proven track record of writing high-performance, multithreaded code for robotics applications
  • Experience with common robotics frameworks (e.g., ROS/ROS2, DDS)
  • hands-on experience with modern optimization libraries relevant to motion planning (e.g., Ceres, IPOPT, OSQP)
  • Proven ability to systematically test and debug systems on physical robots
  • integrate perceived environment data (LiDAR, camera, depth sensing) into the planner

Nice to have

  • Experience training and deploying Reinforcement Learning (RL) agents for complex locomotion behaviors
  • Hands-on experience implementing Model Predictive Control (MPC) or similar optimization-based control techniques for dynamic robot locomotion
  • Familiarity with perception pipelines and the integration of perceived environment data into the planning stack
  • Experience with GPU-accelerated spatial data structures (e.g., NVBlox, specialized CUDA implementations) for high-throughput, low-latency map updates and querying
  • Strong foundational knowledge of robot kinematics, dynamics, controls, and state estimation (e.g., EKF, particle filters)
  • Experience with multi-robot coordination/route planning and abiding by boundary constraints in a workcell map
  • Experience with multi-robot mapping and localization, including map persistence and sharing capabilities
  • Publications in top-tier robotics conferences (ICRA, RSS, IROS, CoRL)

What the JD emphasized

  • real-time motion planning
  • navigation systems
  • autonomous mobile platforms
  • 3D/volumetric map representations
  • locomotion-specific path and motion planning algorithms
  • modern C++ (C++17/20)
  • high-performance, multithreaded code for robotics applications
  • common robotics frameworks (e.g., ROS/ROS2, DDS)
  • modern optimization libraries relevant to motion planning
  • systems on physical robots
  • integrate perceived environment data (LiDAR, camera, depth sensing) into the planner

Other signals

  • humanoid robots
  • autonomous operation
  • logistics and manufacturing environments
  • scaling commercial deployments
  • real-time motion planning
  • navigation systems