Robotics Simulation Expert (human)

The role

You will build, configure, and operate complex robotics simulations. You will develop benchmark environments, design reproducible experiments, and analyze the transferability of simulation results to real robot platforms.

What you will do

• Build, configure, and operate complex robotics simulations
• Develop standardized benchmark environments and test cases
• Run regular in-the-loop tests
• Evaluate control, planning, and learning approaches against metrics
• Design reproducible simulation and evaluation experiments
• Conduct systematic ablation studies
• Ensure versioning and documentation of scenarios, configurations, and results
• Analyze transferability of simulation results to real robot platforms
• Identify and quantify sim-to-real gaps
• Calibrate simulation parameters for physical realism

Required qualifications

• Bachelor's or Master's degree in Robotics, Computer Science, Mechanical Engineering, Mechatronics, Computational Engineering, or a related field
• At least three years of relevant professional experience in robotics simulation, benchmarking, or model-based evaluation
• Proven hands-on experience in research or industry
• Strong expertise in MuJoCo (contact models, actuation, sensors, XML models)
• Experience with multi-GPU-accelerated simulation environments such as NVIDIA Isaac Sim or PyBullet
• Confident handling of robot and environment modelling (URDF, MJCF, USD)
• Solid understanding of robotic dynamics, kinematics, and contact physics
• Experience developing objective metrics and statistically evaluating simulation results
• Knowledge of structured experiment management, Python-based toolchains, and automation of simulation tests
• Understanding of real robot hardware and its limitations
• Team spirit, a structured working style, and the ability to document and communicate technical results with precision

Nice to have

• Experience with or strong interest in sim-to-real transfer methods

About the company

NEURA Robotics develops robotics of the future using simulation to evaluate learning and control methods.