AI-Powered Humanoid Robot Specialist

1 day ago

Kozhikode, Kerala, India beBeeRobotics Full time ₹ 1,80,00,000 - ₹ 2,50,00,000

Humanoid Robot AI Engineer Job Description

We are seeking an experienced Humanoid Robot AI Engineer to design and develop advanced AI-driven control and decision-making systems for humanoid robots and embodied agents.

Key Responsibilities:

  • Integrate AI models with humanoid robots to enable seamless interaction and real-time feedback.
  • Develop and implement real-time control frameworks that translate AI decision-making into smooth, safe, and efficient motor actions.
  • Create pipelines to align simulation-to-reality and optimize robot learning for real-world deployment.
  • Apply multi-modal AI learning to enhance robot perception and adaptability in complex environments.
  • Collaborate with hardware teams to calibrate robot sensors, optimize energy efficiency, and ensure reliable AI control execution.
  • Develop safety protocols for autonomous decision-making in human-centric environments.
  • 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.
  • Strong background in machine learning, particularly reinforcement learning, imitation learning, or embodied AI systems.
  • Hands-on experience with robotics simulation environments, such as PyBullet or V-REP.
  • 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 on vision-language-action models.

PREFERRED SKILLS AND QUALIFICATIONS

  • Familiarity with NVIDIA Jetson and real-time robotics inference.
  • Experience with large foundation models adapted for robotics.
  • Knowledge of safety-critical autonomous systems.
  • Exposure to distributed training of large models on RTX/HPC clusters.
  • Working knowledge of teleoperation frameworks.

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