Job Description
At Mentee Robotics, we are redefining humanoid automation with an AI-first approach - combining perception, reasoning, and dexterous manipulation into fully autonomous systems that continuously learn and adapt.
We are now expanding with a new robotics Engineering center in China, working hand-in-hand with our engineering teams in headquarters. Its mission: to rapidly develop our next-generation full-size humanoid and bring it to life - a walking, working platform that becomes the foundation of our next generation of products. This is a small, senior, hands-on team where speed of iteration is the core value.
We are looking for a Sim2Real Engineer to own the bridge between our simulation and our physical humanoid. You will build the digital twin from our actual CAD, model our actuators from real measurements, and close the gap so that policies trained in simulation work on the robot on the first deployments - not the fiftieth.
Who you are?
- A physics-minded engineer who treats simulation fidelity as an engineering discipline, not an afterthought
- Rigorous about system identification - you fit models to measured data
- A natural bridge between the hardware lab and the training cluster
Responsibilities:
- Build and maintain the robot's digital twin (URDF/MJCF/USD) directly from the team's CAD, tracking every hardware revision
- Model actuators from real dyno data: torque-speed envelopes, friction, backlash, latency, and thermal limits - so trained policies never demand what hardware cannot deliver
- Run system identification campaigns against the physical robot together with motion control and V&V
- Design domain randomization strategies and simulation environments in Isaac Sim with the Newton physics engine, together with the RL engineer
- Own the sim-to-hardware deployment pipeline together with the compute platform engineer: export, validation, and on-robot evaluation protocols
- Quantify and continuously reduce the sim-to-real gap with defined metrics
- Generate synthetic data and scenario suites for training and regression testing
Requirements:
- M.Sc. (or equivalent experience) in Robotics, Mechanical/Electrical Engineering, or Computer Science
- 4+ years with physics simulation for robotics: Isaac Sim/Newton, MuJoCo, or equivalent
- Strong Python; solid grasp of rigid-body dynamics, contact modeling, and actuator dynamics
- Hands-on system identification experience against physical hardware
- Experience supporting RL policy transfer to real robots (domain randomization, dynamics randomization, actuator modeling)
Advantages:
- Sim2Real work on legged or humanoid robots (e.g., Galbot, AgiBot, XPeng Robotics, LimX)
- Experience with USD pipelines and CAD-to-sim automation
- Familiarity with Motor Operating Region style actuator-constraint modeling in training
- C++ for high-performance simulation components
- Comfortable communicating technical topics in English with international teams
