Position OverviewAs a Controls Engineer, you are responsible for the robot's neuromuscular system. You will bridge the gap between high-level AI reasoning and low-level motor torque, ensuring our semi-humanoids move with unprecedented fluidity, speed, and safety.
This is a role for a modern controls expert who views "control" not just as a set of equations, but as the interface between learned policies and real-world physics.
What You'll Do- Modern Whole-Body Control: Design and implement whole-body control (WBC) frameworks that produce stable, high-bandwidth motion for redundant, high-DOF semi-humanoid platforms.
- RL-to-Real Integration: Lead the deployment of learning-based controllers (RL, Imitation Learning) onto physical hardware. You will own the "Sim-to-Real" pipeline, ensuring learned behaviors translate into reliable, contact-rich robot interactions.
- Dynamic Characterization: Perform system identification and design calibration processes to characterize high-performance actuators and complex system dynamics.
- High-Fidelity Simulation: Build and optimize simulation environments (MuJoCo, Isaac, Pinocchio) to rapidly evaluate controller performance, stability margins, and failure modes.
- Hardware-Software Co-Design: Collaborate with hardware engineers to define the next generation of robot platforms by quantifying how latency, sensor noise, and mechanical design impact control performance.
- Interactive Tooling: Develop internal observability systems to visualize real-time control behavior, helping the broader AI team understand the physical impact of their models.
What You'll Bring- MS or PhD in Robotics/Controls: Or equivalent "in-the-trenches" experience building high-performance robots.
- Modern Toolkit: Deep understanding of rigid body kinematics, spatial math (SO(3) / SE(3)), and dynamics libraries (e.g., Pinocchio, Drake, or MuJoCo).
- AI-First Mindset: Proven experience with Reinforcement Learning or Imitation Learning for manipulation or locomotion. You know how to wrap a learned policy in a robust safety layer.
- Real-Time Mastery: Proficiency in C++ and Python for latency-sensitive workloads running on edge compute.
- Hands-on Grit: A track record of pushing physical hardware to its limits-faster movements, tighter stability, and better disturbance rejection.
Bonus Points For- Experience with Hybrid Motion-Force Control (Operational Space Control, Inverse Dynamics).
- Deep understanding of low-level motor driver architectures and EtherCAT/CAN communication.
- A portfolio of publications at RSS, CoRL, or ICRA showcasing state-of-the-art robot learning or control.
Don't let a checklist stop you. Data shows that underrepresented groups often only apply if they meet 100% of the criteria. We value problem-solving and grit over keyword matching. If you're passionate about the intersection of geometry and robotics, we want to hear from you-even if you don't check every box.
