Phyiscal AI Engineer

Physical AI Engineer

Key Responsibilities

• Integrate

  AI models (transformers, diffusion policy networks, LLMs, vision-language models)

  with physical humanoid robots.
• Design real-time

  control frameworks

  that enable AI decision-making to translate into smooth, safe, and efficient motor actions.
• Develop pipelines to align

  simulation-to-reality (sim2real)

  and optimise robot learning for real-world deployment.
• Apply

  multi-modal AI learning

  (vision, audio, haptics, proprioception) to enhance robot perception and adaptability.
• Collaborate with hardware teams to calibrate robot sensors, optimise energy efficiency, and ensure reliable AI control execution.
• Develop

  safety protocols

  for autonomous decision-making in environments with humans.
• Conduct experiments in areas such as

  human-robot interaction, autonomous navigation, dexterous manipulation, and multi-agent collaboration

  .
• Research and implement

  emerging paradigms in Physical AI

  , including embodied GPTs, action-conditioned transformers, and world-model-based learning.

Required Qualifications

• Bachelor’s or Master’s degree in

  Robotics, Mechatronics, Computer Science, AI, or a related field

  (Ph.D. preferred for senior positions).
• Strong background in

  machine learning

  , particularly reinforcement learning, imitation learning, or embodied AI systems.
• Hands-on experience with

  robotics simulation environments

  (Isaac Sim, MuJoCo, Unity, Gazebo).
• Proficiency in

  Python

  and good knowledge of

  C++/ROS2

  for robotics integration.
• Deep understanding of

  robotics control theory, kinematics, and sensor fusion

  .
• Demonstrated work with

  humanoid robots, quadrupeds, or robotic arms

  , preferably on vision-language-action models.

Preferred Skills

• Familiarity with

  NVIDIA Jetson, CUDA optimisation, and real-time robotics inference

  .
• Experience with

  large foundation models

  adapted for robotics (OpenAI VLA, Google RT-2, Gr00T).
• Knowledge of

  safety-critical autonomous systems

  .
• Exposure to

  distributed training

  of large models on RTX/HPC clusters.
• Working knowledge of

  teleoperation frameworks

  (for collecting demonstration data).

What We Offer

• The chance to work on

  cutting-edge humanoid robotics with embodied AI

  .
• Access to advanced computing infrastructure (

  NVIDIA RTX 6000 GPUs, Jetson Thor platforms, VR mocap systems, and Unitree robots

  ).
• A multi-disciplinary team environment spanning

  AI research and robotics engineering.

• Competitive remuneration and growth opportunities for leadership in

  next-gen Physical AI projects

  .