Role DescriptionWe are looking to recruit an exceptional
Motion Planning & Controls Engineer to develop the software that brings our robot hardware to life, implementing planning and control algorithms that provide purposeful, performant, safe, reliable, and beautiful coordinated motion.
In this role you will:
- Cover the entire software stack that governs robot motion, from optimization-based motion planning and control for an overactuated autonomous system, down to individual motor torque control (and everywhere in between).
- Utilize deep expertise in and intuition for complex, high degree-of-freedom electromechanical systems.
- Employ a true multi-disciplinary mindset, bridging mechanical, electrical, software, and systems engineering domains to solve real-world problems.
- Interface with perception, intelligence, simulation, and platform software systems in designing thoughtful functional architectures that ensure world-class overall robot system performance.
- Have an unrelenting drive to achieve exceptional motion quality via continual test, measurement, analysis, and improvement.
QualificationsMust-have:
- MS or PhD in engineering, mathematics, computer science or a related discipline.
- Experience in optimization-based motion planning and control algorithms such as Model Predictive Control.
- Experience in control, estimation, modeling, and analysis of complex. multi-degree-of-freedom electromechanical systems, including forward/inverse kinematics, forward/inverse dynamics, and task/joint space control.
- Practical experience with motor servo control (position/velocity/torque control) architectures and tuning.
- Proficiency in Python and C++ programming, using up-to-date software development practices and tooling.
- Self-starter attitude with strong ability to identify problems, prioritize them, then plan and execute working solutions.
- Enthusiasm for working in a fast paced startup environment and eagerness to support the team on a variety of topics.
Nice-to-have:
- Familiarity with ground vehicle localization, mapping, and state estimation techniques (e.g. SLAM, Kalman filtering).
- Familiarity with robotic simulation software frameworks/environments.
- Familiarity with modern approaches to robotic manipulation, including state-of-the-art reinforcement learning (RL) and imitation learning (IL) approaches and their application to real-world robots.
- Knowledge of brushless motor current control (e.g. field-oriented control).
- Familiarity with functional safety (FuSa) concepts.
