Phyiscal AI Engineer

We are seeking aPhysical AI Engineerto design, develop, and implement AI-driven control and decision-making systems for humanoid robots and embodied agents. This role involves integratingvision-language-action models, reinforcement learning, imitation learning, and real-time robotics systemsto create robots capable of performing complex tasks in dynamic environments.Key Responsibilities IntegrateAI models (transformers, diffusion policy networks, LLMs, vision-language models)with physical humanoid robots. Design real-timecontrol frameworksthat enable AI decision-making to translate into smooth, safe, and efficient motor actions. Develop pipelines to alignsimulation-to-reality (sim2real)and optimise robot learning for real-world deployment. Applymulti-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. Developsafety protocolsfor autonomous decision-making in environments with humans. Conduct experiments in areas such ashuman-robot interaction, autonomous navigation, dexterous manipulation, and multi-agent collaboration . Research and implementemerging paradigms in Physical AI , including embodied GPTs, action-conditioned transformers, and world-model-based learning.Required Qualifications Bachelor’s or Master’s degree inRobotics, Mechatronics, Computer Science, AI, or a related field(Ph.D. preferred for senior positions). Strong background inmachine learning , particularly reinforcement learning, imitation learning, or embodied AI systems. Hands-on experience withrobotics simulation environments(Isaac Sim, MuJoCo, Unity, Gazebo). Proficiency inPythonand good knowledge ofC++/ROS2for robotics integration. Deep understanding ofrobotics control theory, kinematics, and sensor fusion . Demonstrated work withhumanoid robots, quadrupeds, or robotic arms , preferably on vision-language-action models.Preferred Skills Familiarity withNVIDIA Jetson, CUDA optimisation, and real-time robotics inference . Experience withlarge foundation modelsadapted for robotics (OpenAI VLA, Google RT-2, Gr00T). Knowledge ofsafety-critical autonomous systems . Exposure todistributed trainingof large models on RTX/HPC clusters. Working knowledge ofteleoperation frameworks(for collecting demonstration data).What We Offer The chance to work oncutting-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 spanningAI research and robotics engineering. Competitive remuneration and growth opportunities for leadership innext-gen Physical AI projects .

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