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

NEURA Robotics; Germany (OH); On-site; USD 120,000 - 180,000; Full time

Job summary

NEURA Robotics seeks a seasoned robotics engineer to own dynamic model fidelity, conduct system identification on a real humanoid or mobile robot, and deliver robust real-time state estimation for pose, velocity, and contact state. You will integrate MuJoCo/Isaac Sim models with hardware data and deploy EKF/UKF for RT loop updates.

This role emphasizes C++ production RT work and Python tooling, with collaboration across controls, estimation, and hardware teams to ensure reliable sim-to-real

Qualifications

• MSc or PhD in Robotics, Mechanical Eng, Electrical Eng, or related field.
• 4+ years of experience developing state estimation or system identification on real hardware.
• Experience with excitation trajectory design and regressor fitting.
• Proficiency in C++ for production RT systems and Python for analysis.

Responsibilities

• Own dynamic model fidelity metrics and bridge simulation vs real-hardware behaviour.
• Perform system identification on 4NE-1 hardware.
• Develop and maintain simulation models in MuJoCo and Isaac Sim.
• Implement EKF/UKF for floating-base pose and contact state.
• Support control systems (MPC, WBC) and learned policies through hardware deployment.
• Collaborate with Pinocchio model parameters with Core Robot Software.

Skills

State estimation

System identification

C++ development

Python programming

Robot dynamics

MPC/WBC deployment

Education

MSc or PhD in Robotics or related field

Tools

MuJoCo

Pinocchio

GTSAM

iSAM2

Kalman filtering

Job 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