Senior Staff Engineer, SoC Application Engineering

Job Description

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 RenesasR-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 So Cs .

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
.

• Act as a senior technical lead forR-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.

• 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 usingC 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…