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Locomotion Body Control Engineer; human

Job in Zürich, 8058, Zurich, Kanton Zürich, Switzerland
Listing for: NEURA Robotics
Full Time position
Listed on 2026-06-01
Job specializations:
  • Engineering
    Robotics, Systems Engineer
Salary/Wage Range or Industry Benchmark: 80000 - 100000 CHF Yearly CHF 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Position: Locomotion & Whole-Body Control Engineer (human)
Location: Zürich

Your mission & challenges

We are growing our legged-robotics capability on NEURA's humanoid (4NE-1) 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.

  • Whole-body motion generation and control for floating-base legged platforms — locomotion, balance, contact transitions, and loco-manipulation (walking while manipulating).

  • 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.

  • 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.

  • Whole-body modelling for the platform: floating-base rigid-body dynamics from URDF / MJCF, joint configuration, FK / IK, Jacobians, and mass / Coriolis / gravity computation.

  • Constraint formulation across the MPC and QP layers — contact, friction, torque, joint, kinematic, and stability constraints — with task-hierarchy design appropriate to the platform.

  • Solver performance work across both layers: warm-starting, numerical conditioning, constraint handling, and real-time reliability at 500 Hz – 1 kHz.

  • 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.

  • High-performance C++ for real-time execution;
    Python tooling for analysis, prototyping, and debugging.

What we can look forward to
  • MSc or PhD in robotics, controls, mechanical or electrical engineering, computer science, or a related field.

  • 4+ years of hands-on experience developing trajectory optimization, MPC for locomotion, and / or whole-body control on physical robots.

  • Strong foundation in floating-base articulated rigid-body dynamics and contact modelling.

  • Strong working knowledge of reduced-order locomotion models (LIPM, SRBD, centroidal dynamics, or equivalents) and their use inside MPC.

  • Strong foundation in optimal control, constrained numerical optimization, and model-predictive control for legged robots.

  • Hands-on experience with whole-body QP / TSID frameworks on real robot data — including QP / DDP solver internals.

  • Hands-on experience deploying real-time control / MPC / WBC pipelines at 500 Hz – 1 kHz on hardware.

  • Strong C++ for real-time robotics software;
    Python for analysis, tooling, prototyping, and debugging.

  • Practical understanding of how contact dynamics, actuator limits, latency, state-estimation error, solver failure modes, and model mismatch behave on real hardware.

  • A collaborative working style: shared design, constructive code review, proactive communication, and reliable coordination across control, estimation, simulation, low-level control, and hardware disciplines. Strong teamwork is essential for this role.

Nice to Have
  • Hands-on experience on humanoids, quadrupeds, or other high-DOF legged robots.

  • Familiarity with Pinocchio, Mu Jo Co , Crocoddyl, IPOPT, TSID, OCS2, or similar open-source tools.

  • Hierarchical QP, weighted QP, task prioritization, contact force optimization, or operational-space control.

  • Contact planning, gait optimization, balance recovery; CPG-based or hybrid CPG / MPC controllers.

  • Multi-contact WBC: foot contact, bimanual grasping, or base-arm coordination.

  • Contact-consistent dynamics and impact-aware control transitions.

  • Experience with torque-controlled robots and high-bandwidth electric actuation.

  • Publications at RSS, ICRA, IROS, or CoRL in legged locomotion or whole-body control.

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