Dexmate
Dexmate is at the forefront of developing advanced robotic systems that solve real-world challenges. We're building next-generation robots designed to work alongside humans, operate in human environments, and help address growing labor shortages.
Position Overview
We are seeking talented Control Engineers to join our dynamic team and lead the development of state-of-the-art control and state estimation algorithms for our robot platform.
Key Responsibilities
- Develop and implement state estimation, sensor fusion, planning, control algorithms that enable fast, dynamic and safe robot motion
- Collaborate with cross-functional teams including embedded system, perception, hardware, AI
- Optimize control performance across multiple domains including stability, safety, precision, and energy efficiency
- Design and conduct experiments to validate control algorithms both in simulation and on hardware
- Analyze system performance data to identify failure modes and improvement opportunities
- Document technical approaches, implementation details, and experimental results
Requirements
- Master's or PhD in Robotics, Controls, Mechanical Engineering, or related technical field
- 4+ years of professional experience developing control systems for dynamic robots
- Strong expertise in control theory including nonlinear control, model predictive control, and optimal control
- Experience with state estimation techniques such as Kalman filters, particle filters, and factor graphs
- Proficiency in C++, Python, Rust for real-time robotics applications
- Strong understanding of robot kinematics, dynamics, and mathematical modeling
- Experience working with sensor integration including IMUs, encoders, force/torque sensors
- Proven track record of implementing and testing control algorithms on physical robotic systems
- Excellent problem-solving skills and ability to debug complex system interactions
Preferred Qualifications
- Experience with highly dynamic control systems such as bipedal, quadruped, or humanoid robots
- Knowledge of reinforcement learning or other machine learning approaches for control
- Experience with whole-body control and contact dynamics for legged systems
- Experience with real-time computing and optimization
- Background in trajectory optimization and motion planning
- Familiarity with ROS, simulation environments (e.g., Drake, Isaac Sim, SAPIEN, MuJoCo, PyBullet)
- Track record of publications in top-tier robotics conferences/journals