Senior Physical AI Engineer

Responsibilities

• Design and develop intelligent robotic systems powered by modern AI and machine learning techniques
• Build scalable training pipelines for embodied AI and robot learning applications
• Develop production-grade robotics software in Python and related frameworks
• Train and deploy learning-based robotic policies using reinforcement learning and imitation learning approaches
• Integrate AI models with physical robot actuators, camera systems, and sensor pipelines
• Implement robotics control algorithms, kinematics, dynamics, and sensor fusion systems
• Work with simulation and synthetic data generation environments including Isaac Sim, MuJoCo, Sapien, or similar platforms
• Optimize low-latency inference and control systems for real-world robotic deployment
• Collaborate across robotics, AI, systems, and hardware teams to bring intelligent robotic platforms into production
• Contribute to the architecture of next-generation embodied AI systems

Requirements

• 5+ years of industry experience in robotics, embodied AI, physical AI, or autonomous systems
• Bachelor's degree in Computer Science, Artificial Intelligence or relevant field
• Strong Python programming skills with experience building scalable production systems
• Hands-on experience building, training, and deploying machine learning systems for robotics applications
• Deep familiarity with reinforcement learning, imitation learning, and modern ML frameworks such as PyTorch
• Strong understanding of robotics fundamentals, including: - robot control systems - kinematics and dynamics - perception and sensor fusion - camera and tactile sensor integration
• Experience with robotics middleware such as ROS or ROS2
• Experience working with simulation environments such as NVIDIA Isaac Sim, MuJoCo, Sapien, or equivalent platforms
• Strong debugging and systems engineering skills across software and hardware boundaries
• Experience deploying robotics systems into real-world physical environments

Preferred Qualifications

• Experience with humanoid robotics or manipulation systems
• Background in autonomous systems or embodied foundation models
• Experience with GPU acceleration and distributed training systems
• Familiarity with synthetic data generation and sim-to-real transfer techniques
• Experience optimizing robotics systems for edge or embedded deployment