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Principal AI Engineer - Unmanned Autonomous Systems (Internal Use)

Principal AI Engineer - Unmanned Autonomous Systems (Internal Use)

TalentsisSingapore, Singapore, SG
30+ days ago
Job type
  • Quick Apply
Job description
  • 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.
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