Physical AI Engineer

Job Details: Job Description: Responsible for the overall design and development of integrated Artificial Intelligence (AI) solutions for deep learning and machine learning systems that integrate hardware, software, firmware, board, and silicon components with specific focus on customer requirements and implementation limitations throughout the systems lifecycle. May also be responsible for AI systems architecture and definition, including translating the business opportunity into use cases and developing product specifications for required hardware and software needed to deliver system requirements. Impacts and influences the AI product roadmap and development based on profound comprehension of AI and deep learning algorithms, deep learning customer requirements, and deep learning software frameworks. Impacts related technologies/components such as memory, security, and OS that may be central to the final solution. Develops new methods in the areas of reinforcement learning, policy learning, computer vision, machine learning, simulation, sim2real, autonomous driving, and robotics. Leads design, analysis, and implementation of componentlevel choices across the integrated AI systems on performance, features, and cost, including analysis of risks and emphasis on ease of use, reliability, security, availability, maintainability, sustainability, and quality. Defines systems implementation and integration approach and plans to ensure optimum performance and reliability across hardware and software that comprise the system. Delivers endtoend technical solutions to solve customer problems, deploying solutions, executing benchmark tests, and preparing documentation. Conducts analysis and makes reliable engineering recommendations to ensure reliability/resiliency of the AI infrastructure. Monitors and reports on utilization and plans continuous process improvement. Collaborates with other teams to analyze next generation requirements and opportunities and may influence and guide research and academic collaboration in the space of cloud systems and solutions, including proofofconcept and solutions beyond current industry approaches. Simulates reallife environments in the cluster environment and analyzes performance of prototypes. Contributes applied/customer knowledge to AI roadmap working with AI system architects. Qualifications: Qualifications Education Background • Master's, or PhD in Computer Science, Robotics, Artificial Intelligence, Automation, Electronic Engineering, or related majors. • Project and work experience related to robotics, reinforcement learning, multimodal AI, or systems technology. • Fresh graduates with project experience from top-tier domestic university robotics laboratories may be considered as appropriate. Algorithm and Programming Skills • Solid programming foundation, proficient in Python; familiarity with C++ is preferred. • Proficient in at least one deep learning framework: PyTorch, TensorFlow, or Jax. • Experience with projects or papers in directions such as Embodied AI, RL, VLA, or Multimodal learning is preferred. System and Low-level Technical Skills • Familiar with Linux operating systems; experience with Shell, system tuning, or drivers/kernel basics is preferred. • Experience with CUDA (e.g., kernel calls, TensorRT, memory management, etc.) is preferred. • Familiar with or willing to deeply learn inference frameworks such as Intel OpenVINO, ONNX Runtime, and OpenCL. • Experience in performance optimization, parallel computing, system profiling, or compilers/toolchains is a plus. Robotics Platform and Engineering Experience • Experience with ROS / ROS2 or robotic hardware debugging is preferred. • Experience with simulation platforms such as Isaac Sim, MuJoCo, or PyBullet is preferred. Soft Skills • Possess strong learning ability, teamwork spirit, and communication skills in English. • Research spirit, problem-solving ability, and an awareness of engineering implementation ... (truncated, view full listing at source)