Company
Full-StackSolutionEngineer:HumanoidWhole-BodyControlandLoco-manipulation
Neural analysis suggests this role is
optimal for Mid candidates.
“Full-Stack Solution Engineer: Humanoid Whole-Body Control and Loco-manipulation. Skills: Whole-Body Control, Loco-manipulation, Policy Deployment, Robot Testing. Build software for motion tracking. Improve software for loco-manipulation”
What You'll Achieve.
Ensure motion commands become stable movement; Ensure motion commands become expressive movement; Ensure motion commands become feasible movement; Achieve low-latency execution; Achieve reliable execution
Industry & Context.
Diagnose and close sim-to-real gap; Debug failures across stack
What They're Looking For.
Must Have
PhD in Robotics, Machine Learning, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field (or equivalent experience), at least 3 years of research and engineering experience, Reinforcement Learning for Control, Simulation and Synthetic Training Pipelines, Whole-Body Control and Motion Tracking, Hardware-First Robotics Engineering, Systems Programming, Mathematical and ML Foundations
What You'll Do.
Build software for motion tracking
Improve software for loco-manipulation
Deploy control policies on hardware
Optimize runtime stack
Develop planners and interfaces
Convert inputs to motion targets
Diagnose and close sim-to-real gap
Profile and optimize inference
Optimize control-loop timing
Optimize GPU utilization
Optimize robot-side runtime
Debug failures across stack
Full Job Description
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 desi
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