Principal AI Engineer - Robotics Control (Internal Use)

Talentsis; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Talentsis is seeking a seasoned Robotics Control Engineer in Singapore to design and implement advanced control algorithms for autonomous robots. You will optimize motion planning, develop real-time architectures, and collaborate with AI and hardware teams.

Strong background in control theory, state estimation, and MATLAB/Simulink is valued. Applicants should have a PhD/masters with 3+ years in robotics, proficiency in C++, Python, ROS, and experience with PX4/AirSim.

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

Responsibilities

• 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.

Skills

C++

Python

ROS

Control theory

State estimation

Optimization

Real-time control

Education

Master's or PhD in Robotics/Control Systems

Tools

PX4

AirSim

MATLAB/Simulink

Job description

• 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.

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.