Robotics Engineer

About the Role

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.

Responsibilities

• Whole-Body Learning & Control
  • 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
• VLA / WAM & Generalist Policies
  • 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
• Vision-Based Reinforcement Learning
  • 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
• Modeling, State Estimation & Control
  • 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
• Simulation, Data & Training
  • Build humanoid simulation and training environments using MuJoCo, 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
• Real Robot Deployment
  • 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

Qualifications

Must Have

• 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 MuJoCo, 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

Strongly Preferred

• 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

Nice to Have

• Publications or strong project experience in humanoid robotics, robot learning, RL, imitation learning, VLA, or visuomotor control
• Experience with large-scale robot datasets and multi-task policy training
• Experience with dexterous or bimanual manipulation
• Familiarity with foundation models for robotics and embodied AI
• Experience with object detection, tracking, 3D perception, or scene representations
• Experience building production-quality robotics software and deployment infrastructure

Pay

Pay Range: $80,000 - $120,000 per year. The actual base salary offered will depend on factors such as the candidate’s experience, skills, qualifications, and job-related considerations. This position may also be eligible for additional compensation and benefits.