Principal AI Engineer - Robotics Control (Internal Use)

· Design and implement advanced control algorithms (e.g., PID, adaptive control, model predictive control) tailored for autonomous robotic platforms.· Optimize motion planning and path control strategies to enhance multi-robot coordination and operational efficiency.· Develop real-time control architectures that leverage edge computing for low-latency decision-making.· Integrate adaptive learning techniques to continuously refine control performance, particularly for dynamic systems.· Develop and test control algorithms in both simulation and real-world experiments, utilizing aerial simulation tools (e.g., PX4 SITL, AirSim) when applicable.· Collaborate with interdisciplinary teams to integrate control systems with AI, sensor fusion, and hardware subsystems.RequirementsRequired Qualifications· Master’s or PhD in Robotics, Control Systems, or a related field.· 3+ years of experience in robotics control system development or equivalent R&D experience (PhD candidates with strong research contributions are encouraged to apply).· Strong background in control theory, state estimation, and optimization for autonomous systems.· Proficiency in C++, Python, and ROS; hands-on experience with real-time control systems is essential.· Expertise in traditional model-based control methods is required; familiarity with learning-based control approaches (e.g., reinforcement learning, adaptive control) is a plus.· Experience in aerial robotics control—including familiarity with flight dynamics and autopilot systems (e.g., PX4)—is highly desirable.Preferred Qualifications· Postdoctoral experience in robotics or control systems.· Experience with real-world deployment and flight testing of autonomous systems.· A strong research record in control theory, AI-driven robotics, or multi-agent coordination with an emphasis on aerial applications.· Proficiency with MATLAB/Simulink for control system design and testing.

· Design and implement advanced control algorithms (e.g., PID, adaptive control, model predictive control) tailored for autonomous robotic platforms.

· Optimize motion planning and path control strategies to enhance multi-robot coordination and operational efficiency.

· Develop real-time control architectures that leverage edge computing for low-latency decision-making.

· Integrate adaptive learning techniques to continuously refine control performance, particularly for dynamic systems.

· Develop and test control algorithms in both simulation and real-world experiments, utilizing aerial simulation tools (e.g., PX4 SITL, AirSim) when applicable.

· Collaborate with interdisciplinary teams to integrate control systems with AI, sensor fusion, and hardware subsystems.

Requirements

Required Qualifications

· Master’s or PhD in Robotics, Control Systems, or a related field.

· 3+ years of experience in robotics control system development or equivalent R&D experience (PhD candidates with strong research contributions are encouraged to apply).

· Strong background in control theory, state estimation, and optimization for autonomous systems.

· Proficiency in C++, Python, and ROS; hands-on experience with real-time control systems is essential.

· Expertise in traditional model-based control methods is required; familiarity with learning-based control approaches (e.g., reinforcement learning, adaptive control) is a plus.

· Experience in aerial robotics control—including familiarity with flight dynamics and autopilot systems (e.g., PX4)—is highly desirable.

Preferred Qualifications

· Postdoctoral experience in robotics or control systems.

· Experience with real-world deployment and flight testing of autonomous systems.

· A strong research record in control theory, AI-driven robotics, or multi-agent coordination with an emphasis on aerial applications.

· Proficiency with MATLAB/Simulink for control system design and testing.