Artificial Intelligence Engineer

We are seeking a PhD-level Physical AI Engineer to help define the next generation of intelligent computing platforms for robotics, industrial automation, machine vision, autonomous systems, and edge AI. This role will drive system architecture and workload analysis across heterogeneous computing platforms that combine CPUs, DSPs, FPGA fabric, AI accelerators, GPUs, memory subsystems, and high-speed sensor interfaces. You will focus on mapping real-world Physical AI workloads to optimized hardware and software architectures while influencing future platform and silicon roadmaps. As a member of an advanced architecture team, you will collaborate across CPU, GPU, real-time processing, video, embedded systems, and AI technologies to shape next-generation heterogeneous computing platforms. This is a highly visible technical leadership opportunity focused on defining future intelligent systems and programmable computing architectures. Key Responsibilities Define end-to-end sensor-to-actuator Physical AI reference architectures. Analyze and partition AI, computer vision, perception, planning, and control workloads across CPUs, DSPs, FPGAs, GPUs, and AI accelerators. Evaluate architecture tradeoffs involving performance, latency, power efficiency, memory bandwidth, scalability, and deterministic execution. Develop workload models, benchmarks, and performance projections for robotics, autonomous systems, and edge AI applications. Collaborate with engineering teams, customers, and ecosystem partners to identify future platform requirements. Influence next-generation SoC, FPGA, accelerator, memory, and interconnect architectures. Required Qualifications PhD in Electrical Engineering, Computer Engineering, Computer Science, Robotics, or a related technical discipline. Experience in system architecture, embedded computing, heterogeneous computing, robotics, AI acceleration, or advanced compute platforms. Strong understanding of heterogeneous compute architectures including: CPUs DSPs FPGAs GPUs AI/ML accelerators Memory subsystems High-speed interconnects Experience with one or more of the following: Artificial Intelligence / Machine Learning Computer Vision Robotics Autonomous Systems Edge AI Demonstrated technical leadership with the ability to influence architecture decisions and long-term technology strategy. Preferred Qualifications Experience with ROS2, edge AI frameworks, industrial robotics, machine vision, or autonomous platforms. Knowledge of FPGA-based acceleration and heterogeneous computing systems. Publications, patents, or recognized technical contributions in AI, robotics, computer architecture, or embedded systems. Experience modeling performance, latency, memory bandwidth, and power efficiency for complex AI workloads. #J-18808-Ljbffr iTCO Solutions