Principal AI Engineer - Unmanned Autonomous Systems (Internal Use)
AI Summary
Principal AI Engineer designing and integrating mechanical, electrical, and software components for autonomous robotic unmanned systems, developing navigation, perception, and control algorithms.
About this role
· Design and integrate complex mechanical, electrical, and software components for autonomous robotic systems.
· Develop and implement advanced algorithms for navigation, obstacle avoidance, and coordinated mission execution.
· Create multi-modal perception systems using sensor fusion (e.g., LiDAR, cameras, IMUs) for environmental mapping and object recognition.
· Integrate flight controllers and autopilot systems (e.g., PX4) into system architectures to ensure seamless communication and control.
· Conduct system-wide debugging, integration testing, and performance tuning in both simulation (e.g., AirSim, Gazebo SITL with PX4) and real-world environments.
· Develop methodologies for fault detection, redundancy, and failure recovery to enhance system reliability.
· Optimize overall system performance and energy efficiency for extended operations under dynamic conditions.
· Collaborate with interdisciplinary teams (AI researchers, control engineers, hardware designers) to ensure seamless system functionality.
· Prototype, test, and iterate on novel autonomous capabilities in simulation and field environments.
Requirements
Required Qualifications:
· Master’s or PhD in Robotics, Mechanical Engineering, AI, or a closely related field.
· 3+ years of hands-on experience in autonomous systems development or equivalent R&D experience (strong research records from PhD candidates are encouraged).
· Proficiency in C++ and Python; experience with ROS is a plus.
· Strong background in sensor fusion, SLAM, and multi-agent coordination.
· Demonstrated experience with flight controllers or autopilot systems (e.g., PX4) in robotic platforms is highly desirable.
Preferred Qualifications:
· Postdoctoral research experience in robotics, autonomous systems, or related fields.
· Experience with AI-driven decision-making and learning-based autonomy.
· Proficiency in simulation platforms (e.g., AirSim, Gazebo SITL with PX4, CoppeliaSim) and rapid prototyping.
· A strong publication record in robotics, AI, or autonomous systems research.
Skills
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