Senior Roboticist

Summary

Join a pioneering team at the forefront of humanoid robotics and embodied AI. We are redefining how intelligent systems learn and interact with their environment by integrating advanced sensors, real-time monitoring, and human training data into our next-generation robotic platforms. Lead the design, integration, and optimization of robotic systems tailored for embodied AI applications, focusing on humanoid platforms. Develop and implement robust sensor fusion techniques and design telemetry systems. Architect solutions for recording, processing, and leveraging human training data to enhance robot learning. Work closely with hardware, simulation, and software teams. Ensure algorithms and models are efficiently deployed on edge devices, addressing constraints like latency and energy consumption. Drive cutting-edge research initiatives and contribute to peer-reviewed publications.

Requirements

• PhD in Electronics, Robotics, Computer Science, or a related field
• Deep expertise in humanoid robotics and embodied AI, with a strong background in multimodal sensing, sensor fusion, and telemetry system design
• Proficiency with robotics middleware and simulation platforms (e.g., ROS2, Gazebo, PyBullet, NVIDIA Isaac)
• Advanced programming skills in Python and C++, with experience in real-time systems and embedded deployments
• Proven research acumen demonstrated through innovative problem-solving, algorithm development, and a track record of scholarly publications
• Excellent communication skills and a collaborative mindset to work effectively across interdisciplinary teams

Responsibilities

• Lead the design, integration, and optimization of robotic systems tailored for embodied AI applications, focusing on humanoid platforms
• Develop and implement robust sensor fusion techniques for diverse inputs (e.g., vision, IMU, tactile, audio) and design telemetry systems to monitor real-time performance and capture training data from human demonstrations
• Architect solutions for recording, processing, and leveraging human training data to enhance robot learning and adaptive behavior
• Work closely with hardware, simulation, and software teams to ensure seamless integration of AI models, sensor platforms, and embedded systems
• Ensure that algorithms and models are efficiently deployed on edge devices and robotic platforms, addressing constraints like latency, energy consumption, and real-time operation
• Drive cutting-edge research initiatives, contribute to peer-reviewed publications, and continuously push the boundaries of what humanoid robots can achieve

Preferred Qualifications

• Experience in integrating AI models for robotics applications, including reinforcement learning and data-driven control systems
• Familiarity with human-robot interaction protocols, safety standards, and ethical considerations in autonomous systems
• Exposure to cloud-based systems and edge computing to support scalable robotic deployments