Senior Staff Engineer, SoC Application Engineering

Key Responsibilities

• Act as a senior technical lead for R-Car based humanoid robotics, physical AI, autonomous systems, and advanced robotic platforms.
• Lead collaboration with the Physical AI & Humanoid Lab to develop robotic platforms, prototypes, proof-of-concepts, and demonstrations.
• Lead ecosystem partner project execution to develop robotic reference designs and reusable customer solutions.
• Support customer and partner programs from architecture and platform bring-up through software integration, debugging, performance optimization, validation, and production.
• Lead root-cause analysis of complex hardware/software issues across: Linux/Ubuntu ROS2 and robotics middleware, GPU and AI accelerators, device drivers and firmware, memory management and SMMU/IOMMU, IPC and heterogeneous multicore systems, AI runtimes and inference pipelines, system performance, latency, and memory bandwidth.
• Analyze customer robotic architectures and requirements against R-Car capabilities and identify technical gaps, risks, and recommended solutions.
• Support customer RFI/RFQ activities, architecture reviews, design reviews, and technical debug sessions.
• Develop robotic proof-of-concepts, demonstrations, reference applications, and benchmarks showcasing R-Car SoC capabilities.
• Provide technical expertise in GPU compute and heterogeneous processing, including CPU/GPU/NPU workload partitioning, synchronization, memory movement, and performance optimization.
• Support GPU and compute technologies such as Vulkan, Vulkan Compute, OpenCL, OpenGL ES, and OpenVX.
• Analyze and optimize system performance across CPU, GPU, NPU, DSP, memory, and other hardware accelerators.
• Support robotic workloads including perception, sensor fusion, localization, mapping, motion planning, navigation, manipulation, and AI inference.
• Work with Renesas global engineering teams, customers, and ecosystem partners to define requirements for future R-Car SoCs, AI software, tools, and robotic reference platforms.

Qualifications

• Bachelor's, Master's, or Ph.D. degree in Computer Engineering, Electrical Engineering, Computer Science, Robotics, or a related field.
• 10+ years of experience in embedded systems, semiconductor application engineering, robotics, automotive electronics, or related fields.
• Strong embedded development and debugging experience using C and C++.
• Deep understanding of embedded SoC architecture, including CPU, GPU, memory, firmware, operating systems, drivers, middleware, and hardware accelerators.
• Strong hands-on experience with embedded Linux and complex system-level debugging.
• Experience with BSPs, bootloaders, Linux kernel, device drivers, firmware, and user-space applications.
• Strong understanding of heterogeneous multicore architectures, memory management, IPC, DMA, shared memory, and hardware accelerators.
• Hands-on knowledge of GPU architecture and GPU compute.
• Experience with one or more of Vulkan, OpenCL, OpenGL ES, Vulkan Compute, or OpenVX.
• Experience with system and GPU performance analysis using tracing, profiling, logging, and debugging tools.
• Familiarity with embedded AI/ML workloads and deployment on heterogeneous SoCs.
• Strong problem-solving, customer-facing, and technical communication skills.

Preferred Qualifications

• Experience with Renesas R-Car or comparable high-performance SoCs.
• Strong experience with Ubuntu Linux.
• Hands-on experience with ROS2, including application development, integration, debugging, and deployment.
• Experience developing or integrating humanoid robots, autonomous mobile robots, robotic manipulators, or other physical AI systems.
• Experience with robotic perception, sensor fusion, localization, mapping, motion planning, navigation, or control.
• Experience with Imagination/PowerVR or comparable embedded GPU architectures.
• Experience with GPU profiling and system performance tools.
• Experience with embedded AI frameworks and runtimes such as PyTorch, TensorFlow, ONNX, OpenCV, or vendor-specific AI SDKs.
• Experience optimizing AI models and workloads for embedded inference.
• Experience integrating robotic sensors including cameras, LiDAR, radar, IMUs, encoders, or force/torque sensors.
• Experience developing robotic demonstrations, proof-of-concepts, reference platforms, or semiconductor reference designs.