Humanoid Robotics Engineer

Position Overview

We are seeking a skilled
Humanoid Robotics Engineer
to lead the training, development, and commercial deployment of advanced humanoid robots, including Unitree G1, H1, and similar platforms. This role focuses on advancing AI-driven learning capabilities, implementing reinforcement learning algorithms, and preparing humanoid systems for real-world commercial applications across manufacturing, logistics, healthcare, and service industries.

Key Responsibilities:

Robot Training & Development

• Design and implement
  reinforcement learning (RL) control routines
  for humanoid robot locomotion, manipulation, and task execution.
• Develop imitation learning algorithms
  to enable robots to learn complex behaviours through human demonstration.
• Create and optimise sim-to-real transfer protocols
  using Isaac Gym, MuJoCo, and other simulation platforms.
• Implement Vision-Language-Action (VLA) models
  for natural human-robot interaction and instruction-following capabilities.

Commercial Application Development

• Deploy humanoid robots in
  manufacturing environments
  for assembly, quality control, and material handling tasks.
• Configure robots for logistics and warehousing
   operations, including inventory management, order fulfilment, and last-mile delivery.
• Adapt systems for retail and customer service
  applications with a focus on safety and user experience.
• Develop hazardous environment applications
  for inspection, maintenance, and emergency response scenarios.

Technical Implementation

• Program and calibrate
  DOF humanoid systems
  with advanced dexterous manipulation capabilities
• Integrate 3D LiDAR, depth cameras, and tactile sensor arrays
  for enhanced environmental perception.
• Implement whole-body control systems
  for balanced locomotion and coordinated upper/lower body movements.
• Optimise battery management and power efficiency
  for extended operational periods (2-4+ hours)

AI & Machine Learning

• Develop and tune
  deep reinforcement learning models
  using frameworks like PyTorch and TensorFlow.
• Create behaviour trees and decision-making algorithms
  for autonomous task planning.
• Implement SLAM (Simultaneous Localisation and Mapping)
  and navigation systems.
• Design object recognition and manipulation strategies
  using computer vision and tactile feedback.

Required Qualifications:

Education & Experience

• Bachelor's degree
  in Robotics Engineering, Mechatronics, Computer Science, Electrical/Mechanical Engineering, or related field.
• 3+ years
  of hands-on experience with humanoid or bipedal robotics systems.
• 2+ years
  of experience with reinforcement learning and machine learning applications in robotics.

Technical Skills

• Expert proficiency
  in Python, C++, and ROS 2 for robotics development.
• Advanced knowledge
  of reinforcement learning frameworks (Isaac Gym, OpenAI Gym, Stable Baselines).
• Experience
  with simulation platforms: MuJoCo, Gazebo, NVIDIA Isaac, Unreal Engine.
• Proficiency
  in machine learning libraries: PyTorch, TensorFlow, scikit-learn.
• Strong background
  in control theory, kinematics, dynamics, and motion planning.
• Hands-on experience
  with sensor integration (LiDAR, cameras, IMUs, tactile sensors).

Specialized Knowledge

• Deep understanding
  of humanoid robot mechanics, joint control, and balance systems.
• Experience
  with force-position hybrid control and impedance control for manipulation tasks.
• Knowledge
  of commercial deployment challenges, including safety protocols and regulatory compliance
• Familiarity
  with Unitree robotics platforms (G1, H1 series) and their development ecosystems

Preferred Qualifications

• Master's degree
  in Robotics or related field with a thesis focus on humanoid systems.
• Experience
  with NVIDIA GR00T platform and humanoid development workflows.
• Previous commercial deployment
  experience in manufacturing, logistics, or service robotics.
• Knowledge
  of industry standards and safety regulations for human-robot collaboration.
• Publication record
  in robotics conferences or journals.