Jobs / United States / Renesas Electronics America INC
Staff Engineer, Software – Physical AI
Renesas Electronics America INC · 🇺🇸 Farmington Hills, MI, US
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 56 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 18 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Renesas Electronics America INC paid sponsored hires in similar roles1 certified filing for “Senior Staff AI Compiler Engineer” (Software Developers) in TX: $225k–$225k, median $225k. Most were filed at wage level IV (100%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
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Sponsor Radar — Renesas Electronics America INC
The US Department of Labor certified 56 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 18 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Renesas Electronics America INC →
About the role
Farmington Hills, MI - Hybrid 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. Key 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. Required Qualifications • 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: u