$ cat jobs/senior-staff-engineer-soc-application-engineering-renesaselectronics-d55b291f3f6c.json
Senior Staff Engineer, SoC Application Engineering
The High Performance Compute (HPC) SoC Business Division is seeking a Senior Staff Engineer, SoC Application Engineering – Robotics & GPU to provide technical leadership for customer and partner development using the Renesas R-Car SoC family. This position will play an integral role in the global effort to position Renesas as the premier semiconductor partner for humanoid robot subsystems , winning design-ins at strategic accounts, distribution partners, and mass-market customers through targeted engineering, optimized AI solutions and workflows, and strategic ecosystem partnerships. A primary focus of this role will be enabling the brain, AI, perception, compute, and control functionality of humanoid and advanced robotic platforms using R-Car SoCs . The successful candidate will combine strong embedded SoC expertise with hands-on experience in robotics, GPU and heterogeneous compute, AI workloads, system-level debugging, and performance optimization . 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 sol
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