Full-Stack Solution Engineer: Humanoid Whole-Body Control and Loco-manipulation

We are building the behavior foundation models for humanoid robots. As a Full-Stack Engineer for Humanoid Whole-Body Control and Loco-manipulation, you will help train large-scale controllers and use the controller as a reliable behavior foundation on real humanoids. You will work across simulation, policy deployment, kinematic planning, and robot testing to ensure that high-level motion commands become stable, expressive, and physically feasible whole-body movement.

What you'll be doing:

• Humanoid Whole-Body Control: Build and improve software for large-scale motion tracking and loco-manipulation in simulation. 

• Policy Deployment on Robot Hardware: Deploy learned whole-body control policies on humanoid platforms and optimize the runtime stack for low-latency, reliable execution.

• Real-Time Kinematic Motion Planning: Develop planners and interfaces that convert task-level inputs, joystick commands, teleoperation signals, human motion, or VLA outputs into motion targets that a whole-body policy can track.

• Simulation-to-Real system-id: Work across simulation, hardware-in-the-loop testing, and physical robot experiments to diagnose and close the gap between policy behavior in simulation and behavior on hardware.

• Performance and Reliability Engineering: Profile and optimize inference, control-loop timing, data flow, GPU utilization, and robot-side runtime performance.

• Robot Testing and Debugging: Debug failures across the full stack: motion representation, state estimation, policy inference, robot model mismatch, actuator limits, contact behavior, latency, and hardware safety constraints.

What we need to see:

• A PhD in Robotics, Machine Learning, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field (or equivalent experience) with at least 3 years of research and engineering experience.

• Reinforcement Learning for Control: Strong experience with reinforcement learning for robotics, including policy training, reward design, motion tracking, curriculum learning, domain randomization, and sim-to-real deployment.

• Simulation and Synthetic Training Pipelines: Hands-on experience building and scaling robot simulation environments in Isaac Lab or similar platforms. You should be comfortable debugging physics, contacts, sensors, robot models, and large-scale training workflows.

• Whole-Body Control and Motion Tracking: Understanding of humanoid or legged robot control. 

• Hardware-First Robotics Engineering: Practical experience deploying policies or controllers on physical robots. You understand actuator limits, torque control, latency, calibration, thermal limits, sensor noise, contact instability, safety constraints, and why controllers that work in simulation can fail on hardware.

• Systems Programming: Familiar with C++ for real-time robotics systems and strong Python for training, simulation, tooling, and experimentation.

• Mathematical and ML Foundations: Understanding of rigid-body dynamics, neural network architectures, and modern learning-based control methods.