We are building a next-generation humanoid robot platform with high-bandwidth torque-controlled joints and full-body actuation. Our short- to mid-term goal is to achieve robust and reliable locomotion in indoor service and industrial environments.
As a Robotics Algorithm Engineer focused on Locomotion, you will work across simulation, learning-based control, state estimation, and real-robot deployment. This is a highly hands-on role requiring both strong implementation skills and the ability to debug complex real-world robotic behaviors.
We are looking for engineers who not only implement algorithms, but also develop their own technical insights and adapt quickly in a rapidly evolving robotics landscape.
Responsibilities
Locomotion & Learning-Based Control
- Develop and deploy RL-based locomotion policies for humanoid robots
- Design training pipelines including domain randomization and sim-to-real transfer
- Improve policy robustness for indoor service and industrial use cases
- Analyze and debug failure modes from both simulation and real-world testing
Full-Body Control & Modeling
- Work on whole-body control frameworks integrating learned policies
- Understand and leverage robot dynamics models for stability and contact reasoning
- Contribute to state estimation using IMU, joint encoders, and contact sensing
Simulation & Tooling
- Build and maintain locomotion simulation environments (Mujoco / Isaac)
- Design training environments and reward shaping strategies
- Analyze the simulation-real gap and iterate on mitigation strategies
Real Robot Deployment
- Deploy policies to hardware with torque-controlled, high-bandwidth actuators
- Perform real-robot tuning, debugging, and performance optimization
- Work closely with firmware, motor control, and hardware teams
Qualifications
Must Have
- 3+ years of experience in robotics, control, or locomotion-related roles
- Strong C++ and Python programming skills
- Experience applying reinforcement learning to robotics control problems
- Experience deploying algorithms on real robots (not simulation-only)
- Solid understanding of rigid body dynamics and feedback control
- Familiarity with state estimation for legged robots
- Experience working in Linux environments
Nice to Have
- Experience with humanoid or legged robots
- Whole-Body Control or MPC exposure
- Mujoco / Isaac Gym / Isaac Sim experience
- Experience addressing sim-to-real transfer challenges
- Familiarity with Pinocchio, CasADi, or similar tools
- CUDA or large-scale RL training experience
- ROS2 experience
Work Mode
- On-site required
- Up to 10% of travel
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