Locomotion & Whole-Body Control Engineer
Posted 11 May 2026
Advertised as “Locomotion & Whole-Body Control Engineer (human)”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 4+ yrs
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
No salary listed for this role.
Field of studyComputer Science, Control Engineering, Electrical Engineering, Mechanical Engineering, Robotics (or related)
Study levelMaster's or higher
Your mission & challenges
We are growing our legged-robotics capability on NEURA's humanoid (4NE1) and quadruped platforms. This role spans both core layers of the legged control stack: trajectory optimization and MPC for kino-dynamic motion generation, and QP-based instantaneous whole-body control for executing those motions on real hardware at 1 kHz.
The work is focused on contact-rich dynamics, real-time optimization, and reliable execution on physical robots. You will collaborate closely with state estimation, simulation, low-level control, and hardware stakeholders, and with the application teams whose tasks ultimately depend on robust, predictable locomotion and whole-body behaviour.
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Whole-body motion generation and control for floating-base legged platforms — locomotion, balance, contact transitions, and loco-manipulation (walking while manipulating).
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Trajectory optimization and model-predictive control pipelines over robot state, contact schedules, ground reaction forces, centroidal momentum, and joint trajectories — using reduced-order locomotion models such as LIPM, SRBD, and centroidal dynamics.
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QP-based task-space inverse dynamics for executing instantaneous whole-body control from MPC and trajectory-optimization references at 1 kHz on the real robot.
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Whole-body modelling for the platform: floating-base rigid-body dynamics from URDF / MJCF, joint configuration, FK / IK, Jacobians, and mass / Coriolis / gravity computation.
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Constraint formulation across the MPC and QP layers — contact, friction, torque, joint, kinematic, and stability constraints — with task-hierarchy design appropriate to the platform.
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Solver performance work across both layers: warm-starting, numerical conditioning, constraint handling, and real-time reliability at 500 Hz – 1 kHz.
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Deployment, tuning, and debugging of MPC, trajectory optimization, IK, and inverse dynamics pipelines on physical robots — including platform-specific contact-model calibration and validation against real robot data.
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High-performance C++ for real-time execution; Python tooling for analysis, prototyping, and debugging.
What we can look forward to
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MSc or PhD in robotics, controls, mechanical or electrical engineering, computer science, or a related field.
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4+ years of hands-on experience developing trajectory optimization, MPC for locomotion, and / or whole-body control on physical robots.