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NEURA Robotics Linkedin · Posted 2mo ago

Dynamics, System Identification & Estimation Engineer - PLA (human)

Germany

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Indexed description

Your Mission & Challenges

  • Dynamic model accuracy ownership: defining model fidelity metrics and owning the gap between simulation behaviour and real-hardware behaviour across dynamic motion and contact-rich interactions
  • System identification on 4NE-1 hardware: motor constants, joint friction, transmission dynamics — excitation trajectory design, regressor fitting, observability analysis, iterative refinement against hardware data
  • Simulation model authoring and maintenance: MuJoCo and Isaac Sim models that match real-world behaviour under dynamic loading and contact; contact model parameterisation, actuator model calibration
  • Real-time state estimation: floating-base EKF/UKF implementation and tuning for pelvis pose, velocity, and foot contact state at RT loop rates; feeds downstream controllers and loco-manipulation policy inputs
  • Sim-to-real pipeline: parameter estimation loops, hardware-data-driven calibration, validation against motion capture or external reference systems — the continuous feedback loop between hardware campaigns and updated sim models
  • Failure mode ownership: debugging model-accuracy-driven failures — control instability from inaccurate dynamics, estimation drift or bias causing divergence, incorrect contact/force estimation leading to instability in dynamic interactions
  • Cross-team interface: supplying updated Pinocchio model parameters to the WBC and State Estimation Engineers in Core Robot Software; aligning on excitation trajectory designs with the Locomotion and RL/Control Engineers

What We Can Look Forward To

  • MSc or PhD in Robotics, Mechanical Engineering, Electrical Engineering, or a related field with a strong foundation in dynamics, estimation, and control
  • 4+ years of experience developing state estimation or system identification solutions for real-time robotic systems — on real hardware, not simulation-only
  • System identification on physical robotic systems: excitation trajectory design, least-squares or maximum-likelihood regressor fitting, actuator and transmission parameter identification
  • State estimation implementation: EKF or UKF for floating-base pose, velocity, and contact state on a legged or mobile robot platform
  • Rigid body dynamics depth: contact modelling, actuator behaviour, and how model inaccuracies propagate to control instability — not just theoretical familiarity
  • Experience supporting control systems (MPC, WBC) or learned policies (RL) through hardware deployment — understanding how model quality gates policy transfer
  • C++ for production RT systems; Python for analysis, tooling, and calibration pipelines

Nice to Have

  • Humanoid or legged robot hands-on experience — 4NE-1 is a full-size humanoid; bipedal dynamics and contact complexity are directly relevant
  • Differentiable simulators for gradient-based system identification (Brax, DiffTaichi, or comparable)
  • Sim-to-real transfer methodology: domain randomisation, adaptive calibration, residual physics modelling
  • Pinocchio for rigid-body model computation and parameter sensitivity analysis
  • MuJoCo model authoring: MJCF contact parameters, actuator models, tendon dynamics
  • Factor graph-based estimation (GTSAM, iSAM2) for tightly-coupled IMU + kinematics fusion
  • Publications or open-source contributions in legged robot dynamics, system identification, or sim-to-real transfer

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