Robotics Engineer

About The Role

We are building a next-generation humanoid robot platform with high-bandwidth torque-controlled joints and full-body actuation.

We are building a next-generation humanoid robot platform with high-bandwidth torque-controlled joints and full-body actuation.

As a Robotics Algorithm Engineer focused on Humanoid Whole-Body Control
, you will work across VLA / WAM, vision-based RL, whole-body control, simulation, state estimation, and real-robot deployment
. You will develop learning-based systems that coordinate locomotion, manipulation, perception, and full-body motion.

We are looking for engineers with strong implementation skills, solid robotics fundamentals, and the ability to turn research ideas into reliable real-world robot behaviors.

• Develop and deploy learning-based whole-body control policies for humanoid robots
• Coordinate locomotion, balance, torso, arms, and end-effectors
• Integrate learned policies with WBC, inverse dynamics, IK, and optimization-based control
• Develop robust contact-aware behaviors for locomotion, manipulation, and interaction
• Analyze and debug instability, contact failures, coordination issues, and policy failures

• Develop and integrate VLA / WAM models for humanoid control
• Adapt foundation-model-based policies to humanoid embodiment and full-body action spaces
• Connect high-level semantic reasoning with low-level whole-body control
• Design action spaces, observations, policy interfaces, and skill representations
• Explore imitation learning, behavior cloning, diffusion policies, transformers, and RL
• Use teleoperation, demonstration, and robot interaction data for training and fine-tuning

• Develop vision-based RL policies using RGB, depth, proprioception, and onboard sensing
• Build visuomotor policies for locomotion, navigation, mobile manipulation, and whole-body tasks
• Develop visual-proprioceptive representation learning and sensor fusion
• Use privileged learning, teacher-student training, distillation, domain randomization, and sim-to-real
• Improve robustness to environment variation, object variation, appearance changes, occlusion, and sensor noise

• Apply rigid-body dynamics, contact dynamics, and humanoid kinematics to whole-body control
• Develop and integrate state estimation using IMU, encoders, force/contact sensing, and vision
• Work with floating-base dynamics and multi-contact estimation
• Combine learning-based policies with feedback control and model-based methods

• Build humanoid simulation and training environments using Mu Jo Co , Isaac Sim / Isaac Lab, or similar platforms
• Develop scalable RL, imitation learning, and visuomotor training pipelines
• Design tasks, curricula, rewards, domain randomization, and system identification
• Generate and use simulation, teleoperation, demonstration, and real-robot datasets
• Analyze sim-to-real gaps in dynamics, contact, sensing, perception, and actuators

• Deploy whole-body and visuomotor policies on humanoid hardware with high-bandwidth torque control
• Perform real-robot tuning, debugging, system identification, and optimization
• Diagnose failures across perception, policy inference, estimation, dynamics, latency, and low-level control
• Optimize policy inference and control pipelines for real-time execution
• Work closely with perception, firmware, motor control, systems, and hardware teams

• 3+ years of experience in robotics, controls, reinforcement learning, imitation learning, or related fields
• Strong C++ and Python skills
• Experience developing learning-based robot control policies
• Experience deploying algorithms on real robots
• Experience with whole-body control, humanoid robotics, legged robotics, or mobile manipulation
• Hands-on experience with reinforcement learning and/or imitation learning
• Solid understanding of rigid-body dynamics, floating-base systems, contact dynamics, and feedback control
• Experience with Mu Jo Co , Isaac Sim / Isaac Lab, or similar simulation platforms
• Familiarity with state estimation and multimodal sensing
• Strong simulation, algorithm, and hardware debugging skills
• Experience working in Linux environments

• Experience with VLA, WAM, whole-body action models, or generalist robot policies
• Experience with vision-based RL or visuomotor learning
• Experience with transformer-based policies, diffusion policies, behavior cloning, or large-scale imitation learning
• Experience combining RGB / RGB-D observations with proprioception
• Experience with humanoid locomotion and manipulation
• Experience with domain randomization, privileged learning, distillation, and system identification
• Experience with teleoperation and demonstration-data pipelines
• Experience with real-time policy deployment and GPU inference
• Experience integrating learned policies with WBC, MPC, inverse dynamics, IK, or trajectory optimization

• Publications…