Staff Engineer, Software – Physical AI

Renesas Electronics•Farmington Hills, MI

17h•Hybrid

About The Position

The HPC SoC Business Division is seeking a Staff Engineer, Software – Physical AI to drive customer and ecosystem adoption of Renesas R-Car SoCs for next-generation robotic and Physical AI platforms. This role will provide technical leadership for humanoid robots, autonomous systems, embodied AI, and advanced robotic platforms by combining expertise in embedded systems, heterogeneous computing, AI acceleration, robotics software, and the mathematical foundations of Physical AI. The successful candidate will work directly with customers, ecosystem partners, and internal engineering teams to develop reference platforms, optimize AI workloads, and shape future R-Car products.

Requirements

BS/MS/PhD in Computer Engineering, Electrical Engineering, Computer Science, Robotics, Applied Mathematics, or related field.
5+ years of experience in embedded systems, semiconductor application engineering, robotics, AI, autonomous systems, or high-performance compute platforms.
Strong C/C++ software development and debugging experience.
Deep understanding of SoC architecture, memory subsystems, firmware, operating systems, device drivers, middleware, and hardware accelerators.
Extensive experience with Embedded Linux, Ubuntu, BSPs, bootloaders, Linux kernel, and system-level debugging.
Strong understanding of heterogeneous compute architectures, DMA, shared memory, IPC, memory hierarchy, cache behavior, and accelerator architectures.
Hands-on experience with GPU architectures and parallel computing.
Experience with Vulkan, OpenCL, OpenGL ES, Vulkan Compute, OpenVX, or equivalent technologies.
Experience profiling and optimizing system performance using tracing, logging, and debugging tools.
Linear Algebra: vectors, matrices, tensors, transformations, projections, eigenvalues/eigenvectors, matrix decompositions, numerical linear algebra.
Geometry & Robotics Mathematics: coordinate transformations, homogeneous transforms, rotation matrices, quaternions, kinematics, dynamics, trajectory generation.
Optimization & Estimation: multivariable calculus, probability, statistics, numerical optimization, Kalman filtering, state estimation, nonlinear optimization.
AI Computing Fundamentals: understanding of how GEMM, convolution, attention, tensor transformations, interpolation, and other mathematical operators map onto CPU/GPU/NPU hardware.
Ability to analyze computational complexity, memory bandwidth requirements, accelerator utilization, and data movement characteristics of AI and robotics workloads.
Strong communication, customer-facing, and technical leadership skills

Nice To Haves

Experience with Renesas R-Car or comparable high-performance SoCs.
Strong experience with ROS2 and robotics middleware.
Experience developing humanoid robots, autonomous mobile robots, robotic manipulators, or other Physical AI systems.
Experience with perception, sensor fusion, localization, SLAM, navigation, planning, and control algorithms.
Experience with AI frameworks such as PyTorch, TensorFlow, ONNX, and OpenCV.
Experience optimizing AI models for embedded deployment.
Experience with robotic simulation environments such as Isaac Sim, Gazebo, MuJoCo, or equivalent.
Experience with Physical AI, foundation models, reinforcement learning, vision-language-action models, or embodied AI systems.
Experience integrating sensors including cameras, LiDAR, radar, IMUs, encoders, and force/torque sensors.
Experience with PowerVR or comparable embedded GPU architectures.

Responsibilities

Serve as technical lead for Physical AI, humanoid robotics, autonomous systems, and robotic compute platforms based on Renesas R-Car SoCs.
Collaborate with robotics and ecosystem partners to develop reference designs, proofs of concept, demonstrations, and reusable customer solutions.
Support customer programs from architecture definition through platform bring-up, software integration, debugging, optimization, validation, and production.
Analyze and optimize AI and robotics workloads including perception, sensor fusion, localization, mapping (SLAM), planning, navigation, manipulation, control, and AI inference.
Lead system-level debugging across Linux, ROS2, AI runtimes, GPU/NPU accelerators, device drivers, firmware, memory management, IPC, and heterogeneous multicore systems.
Optimize workload partitioning across CPU, GPU, NPU, DSP, and dedicated accelerators while balancing latency, throughput, memory bandwidth, and power consumption.
Support GPU compute technologies including Vulkan, Vulkan Compute, OpenCL, OpenGL ES, and OpenVX.
Develop performance models for AI and robotic systems by analyzing compute intensity, memory movement, data locality, accelerator utilization, and end-to-end latency.
Support customer RFIs, RFQs, architecture reviews, design reviews, technical workshops, and debug engagements.
Work with global engineering teams to define future requirements for R-Car SoCs, AI software, tools, and robotic reference platforms.