Principal Research Engineer - Control, Optimization & Safety Systems
Core
Developing real-time control, optimization, and safety-critical systems for embodied AI and robotics, integrating them with learning-based components in hierarchical architectures.
Role type
Principal Research Engineer (Control, Optimization & Safety)
Builds
Safe, reliable, and controllable robotic systems with bounded behavior
Domain
Robotics, Embodied AI, Control Theory, Optimization
Deliverable
production ML models | product features
Required skills
Control theory, Robotics dynamics, Optimization, Real-time systems, Safety-critical systems, C++, Python, Robotic platforms, Simulators, Top-tier publications
Preferred skills
MPC, Trajectory optimization, Control Barrier Functions, Formal methods, Learning-based control, High-DOF robots
Technologies
C++, Python, MPC, Control Barrier Functions
Responsibilities
Develop control algorithms (e.g., MPC, optimal control) for complex robots; Integrate control with learning-based components in hierarchical systems; Design safety mechanisms (runtime assurance, constraint enforcement); Contribute to real-time system design under strict latency constraints; Enable system-level integration across ML, perception, and systems; Translate safety and control requirements into compute and architecture insights
Seniority
Principal, research direction & architecture definition
