Feeny
Neura Robotics

Dynamics, System Identification & Estimation Engineer - PLA (human) at Neura Robotics (Metzingen, Germany / Riederich, Germany)

Neura Robotics· Metzingen, Germany / Riederich, Germany·

Role details

Work type
Onsite
Employment
Full-Time
Skills
C++PythonEKFUKFSystem IdentificationState EstimationRigid Body DynamicsContact ModellingActuator BehaviourMPCWBCRL

Neura Robotics at a glance

German cognitive-robotics company building humanoid and collaborative robots that see, hear, feel, and learn to work alongside people.

NEURA Robotics designs cognitive robots (humanoids, collaborative arms, and mobile bases) plus the AI and platform that run them, all built in-house. Its flagship 4NE1 humanoid targets industrial workflows and everyday assistance, and the Neuraverse platform links deployed robots into a shared learning network.

~$1.7B+ raised · latest: Series C · up to $1.4B · June 2026 · backed by Tether, Amazon, NVIDIA, Qualcomm

Summary

Own dynamic model accuracy, system identification, and real-time state estimation for humanoid robotics hardware. Develop sim-to-real pipelines and maintain high-fidelity simulation models to support control systems and loco-manipulation policies.

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 4NE1 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 — 4NE1 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

Why work at Neura Robotics

  • Culture: "Passion for Winning" — achievement-driven, self-responsible culture with flat hierarchies. Values include trust, honesty, speed, and "We Are Human" (people at the heart of everything).
  • Flexibility: Flexible working hours, 30 days of vacation, and a hybrid-friendly environment.
  • Team: Over 1,400 employees from more than 45 countries — highly skilled, international team of experts.
  • Growth & Development: Support for personal and professional growth via Udemy training platform and more.
  • Compensation: Competitive salary package with exclusive employee discounts.
  • Perks: Summer parties, company town hall meetings, and regular team celebrations.
  • Impact: Opportunity to work on cutting-edge cognitive robotics and AI that directly addresses labor shortages and makes a meaningful societal impact.
  • Diversity: Explicitly welcomes applications from all backgrounds, emphasizing building a team that reflects the world.
  • Application Process: Fast — initial feedback within a few days, interviews scheduled within two weeks.

Application questions