Job Description

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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