About the job
At Dexmate, we are pioneering the creation of sophisticated robotic systems that tackle pressing real-world challenges. Our mission is to develop the next generation of robots that can seamlessly collaborate with humans, operate in diverse environments, and alleviate increasing labor shortages.
Position Overview
We are on the lookout for skilled Control Engineers to become integral members of our innovative team. You will spearhead the development of cutting-edge control and state estimation algorithms for our robotic platforms.
Key Responsibilities
Design and implement state estimation, sensor fusion, planning, and control algorithms to ensure rapid, dynamic, and secure robot movement.
Collaborate with interdisciplinary teams, including embedded systems, perception, hardware, and AI specialists.
Enhance control performance across various aspects such as stability, safety, accuracy, and energy efficiency.
Plan and execute experiments to validate control algorithms in both simulations and real-world hardware.
Evaluate system performance data to pinpoint failure modes and identify areas for enhancement.
Thoroughly document technical methodologies, implementation specifics, and experimental findings.
Requirements
Master's degree or PhD in Robotics, Control Systems, Mechanical Engineering, or a related technical domain.
Minimum of 4 years of professional experience in developing control systems for dynamic robotic platforms.
In-depth knowledge of control theory, including nonlinear control, model predictive control, and optimal control techniques.
Familiarity with state estimation methods such as Kalman filters, particle filters, and factor graphs.
Proficiency in programming languages such as C++, Python, and Rust for real-time robotics applications.
Solid understanding of robotic kinematics, dynamics, and mathematical modeling principles.
Experience with sensor integrations, including IMUs, encoders, and force/torque sensors.
Demonstrated success in implementing and testing control algorithms on physical robotic systems.
Exceptional problem-solving abilities and expertise in debugging intricate system interactions.
Preferred Qualifications
Experience with highly dynamic control systems, including bipedal, quadrupedal, or humanoid robotics.
Knowledge of machine learning algorithms as applied to robotics.
