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Niche embedded AI SoC and model-compression skillset reduces applicant competition despite mid-level seniority.
Role requires specialized embedded AI and automotive ADAS expertise, limiting cross-industry transferability.
Explicit 6+ years requirement plus mandatory embedded SoC, compiler, and model-compression skills make screening stringent.
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Design, implement, and optimize AI perception systems for automotive ADAS/ADAS applications on embedded hardware.
Deploy and optimize deep learning models on edge AI SoCs including accelerators like DSPs, NPUs, TPUs, or GPUs.
Evaluate AI hardware options and lead software integration, prototyping, benchmarking, and cross-functional collaboration in automotive AI SoC selection.
6+ years of professional software engineering experience.
Minimum 2 years of hands-on embedded software development experience.
Strong programming skills in C and C++, including compiler and debugging techniques.
Degree in Computer Science, Automotive Engineering, Robotics, or related field (B.Tech, Master’s, or Ph.D).
Extensive experience bridging AI models with resource-constrained embedded automotive hardware environments.
Proven ability to optimize AI inference on edge devices and knowledge of AI/ML compilers, instruction sets, and memory architectures (FPGA, DSP, AI processors).
Preference for candidates with automotive radar perception knowledge, NVIDIA AI SoC deployment experience, and direct exposure to ADAS or autonomous driving domains.