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Mid-level metro role with some employer recognition but specialized embedded ML/ADAS skillset, so medium competition.
Highly automotive-embedded and ADAS-specific technical requirements limit cross-industry transferability.
Explicit 4-7 years plus mandatory embedded Linux, drivers, Yocto, Renesas and ML stack increases strictness to high.
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Design, develop, and optimize embedded ADAS software on Renesas R-Car platforms, including SoC bring-up, BSP integration, Linux device drivers, and Yocto-based build systems.
Deploy, optimize, and validate neural network models on embedded AI accelerators (Renesas DRP-AI), performing quantization and inference tuning to meet real-time automotive requirements.
Analyze and improve system performance, memory utilization, and CPU/GPU/accelerator efficiency using profiling and debugging tools, ensuring adherence to automotive standards like MISRA C/C++.
4-7 years of experience in embedded software development for automotive or related domains.
Bachelor's or Master's degree in Electronics, Embedded Systems, Computer Science, or related fields (B.E./B.Tech./M.Tech).
Strong programming skills in Embedded C/C++, experience with Linux device driver development, Yocto build system, SoC bring-up, and BSP integration.
Experience deploying neural network models (ONNX, TensorFlow Lite) on embedded accelerators such as Renesas DRP-AI, with knowledge of model quantization and AI inference optimization.
Expertise in automotive-grade embedded software development with familiarity of standards like MISRA C/C++ and functional safety concepts.
Proven ability to optimize embedded software performance at system and neural network levels on multi-core ARM architectures using SIMD/NEON intrinsics.
Experience collaborating with cross-functional teams including system architects and AI engineers to deliver production-quality ADAS solutions on Renesas R-Car platforms in a Linux embedded environment.