Lead - Autonomy (UAVs)

You will also play a key leadership role: mentoring engineers, driving roadmap decisions, and ensuring high performance and reliability in mission-critical environments.

• Architecture & Development
  

• Design and implement core components of the autonomy stack: state estimation, motion planning, trajectory generation, and control.
  

• Drive the integration of perception, mapping, and localization systems into robust navigation algorithms.
  

• Own the autonomy system architecture, ensuring it meets real-time, safety, and performance constraints.
  

• Optimize for performance on embedded compute platforms (e.g., NVIDIA Jetson, PX4 Companion Computers).
  

• Leadership & Strategy
  

• Define the technical roadmap for the autonomy team in alignment with product and platform needs.
  

• Mentor and manage a team of autonomy, controls, and navigation engineers.
  

• Drive code quality, system reliability, and testing strategy, including simulation-in-the-loop (SITL) and real-world test flights.
  

• Represent the autonomy function in cross-functional meetings with firmware, perception, hardware, and product teams.
  

• Testing & Deployment
  

• Develop simulation environments for autonomy testing (e.g., Gazebo, AirSim, or custom frameworks).
  

• Validate algorithms through real-world flight testing and flight log analysis.
  

• Collaborate with systems integration engineers to tune algorithms on physical platforms under various flight conditions.
  

• Research & Innovation
  

• Stay current with state-of-the-art developments in robotics, drone autonomy, and reinforcement learning.
  

• Evaluate and integrate promising approaches from academia and industry (e.g., learning-based control, SLAM, dynamic path planning).
  

Requirements

• Bachelor’s or Master’s degree in Robotics, Aerospace, Computer Science, or a related field.
  

• 6+ years of experience in robotics, autonomous systems, or UAV software.
  

• Strong expertise in at least 2 of the following:
  

  • SLAM / state estimation
    

  • Motion planning (e.g., RRT*, A*, MPC)
    

  • Control theory (PID, LQR, nonlinear control)
    

• Proficient in C++ and Python, with experience in ROS, PX4/Ardupilot integration, and real-time systems.
  

• Hands-on experience with robot simulators (e.g., Gazebo, AirSim, Isaac Sim) and flight test environments.
  

• Proven track record of deploying autonomy software on physical platforms.
  

Benefits