{"id":97972,"date":"2026-04-08T06:56:17","date_gmt":"2026-04-07T21:56:17","guid":{"rendered":"https:\/\/layla-hr.com\/semi\/97972\/"},"modified":"2026-04-08T06:56:17","modified_gmt":"2026-04-07T21:56:17","slug":"application-engineer-automotive-soc-2","status":"publish","type":"post","link":"https:\/\/layla-hr.com\/semi\/97972\/","title":{"rendered":"Application Engineer (Automotive SoC)"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[66,83,86],"tags":[136],"area":[15],"income":[102,103,104],"checked_slug":[],"class_list":{"0":"post-97972","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-catsub05","7":"category-catsub22","8":"category-catsub25","9":"tag-136","10":"area-area13","11":"income-income01","12":"income-income02","13":"income-income03","14":"entry_article"},"acf":{"company":"\u30eb\u30cd\u30b5\u30b9 \u30a8\u30ec\u30af\u30c8\u30ed\u30cb\u30af\u30b9\u682a\u5f0f\u4f1a\u793e","jobtitle01":"Application Engineer (Automotive SoC)","jobtitle02":"","jobdescription01":"Job Description\n\n\n\nJob Summary\n\nWe are looking for an AI Application Engineer to support the enablement, optimization, and deployment of AI models on automotive-grade SoCs.\n\nIn this role, you will work closely with internal compiler\/runtime teams and external customers to bring AI models from training to optimized inference on embedded NPU\/DSP platforms, with a strong focus on performance, accuracy, and system integration.\n\n\u00a0\n\nKey Responsibilities\n\nAI Model Enablement & Optimization\n\nEnable and deploy AI models (e.g., BEV, object detection, segmentation, classification) on Gen4\/5 SoC platforms with CNNIP\/DSP\/NPU HWA.\nPerform model performance analysis (latency, throughput, multi-core scaling) and identify bottlenecks related to memory bandwidth, scheduling, or operator mapping.\nSupport model optimization workflows, including:\nPost-Training Quantization (PTQ)\nQuantization-Aware Training (QAT) collaboration\nOperator fusion, graph optimization, and execution partitioning\nAnalyze accuracy degradation caused by quantization or operator limitations and propose mitigation strategies.\n\nEmbedded AI Inference & System Integration\n\nIntegrate AI models into embedded runtime environments (Linux \/ QNX).\nDebug issues related to:\nCNNIP\/DSP\/NPU offloading\nMemory allocation \/ IPMMU\nData transfer overhead and multi-core synchronization\nValidate AI workloads on target boards and simulators (SIL \/ HIL).\n\nToolchain & Model Workflow Support\n\nWork with AI compiler and runtime toolchains (e.g., ONNX-based workflows, hybrid compiler, MWMX).\nSupport ONNX model handling, including:\nGraph inspection and modification\nModel segmentation and execution control\nQuantized (QDQ) ONNX models\nDevelop or maintain internal tools and scripts to improve model validation, benchmarking, and customer workflows.\n\nCustomer & Cross-Team Collaboration\n\nAct as a technical interface between customers, internal development teams, and field application engineers.\nSupport customer evaluations, PoCs, and demos on automotive AI platforms.\nProvide technical guidance, documentation, and best practices for AI model deployment.\nContribute to weekly technical reports, issue tracking, and release validation activities.\n\n\nQualifications\n\n\n\nRequired Qualifications\n\nBachelor\u2019s or Master\u2019s degree in Computer Science, Electrical Engineering, Embedded Systems, or have experience in embedded systems.\nSolid understanding of deep learning fundamentals and inference pipelines.\nHands-on experience with AI frameworks such as PyTorch, ONNX, or ONNX Runtime.\nStrong programming skills in Python; working knowledge of C\/C++ is a plus.\nFamiliarity with embedded systems and debugging tools.\nAbility to analyze performance using metrics such as latency, throughput, and hardware utilization.\nGood communication skills in a multi-cultural, cross-functional environment.\n\n\u00a0\n\nPreferred \/ Optional Qualifications\n\n1\u20133 years of experience in embedded systems or AI-related development.\nExperience with AI model training, fine-tuning, or evaluation, especially for:\nComputer vision models (Detection \/ Segmentation \/ BEV)\nAutomotive or robotics use cases\nPractical experience with AI inference optimization on embedded hardware (NPU, DSP, GPU, or CPU).\nFamiliarity with quantization techniques (INT8, calibration methods, QDQ models).\nExperience with automotive SoCs or safety-related software environments (QNX is a plus).\nUnderstanding of memory hierarchy, DMA, and multi-core scheduling in SoC architectures.\n\n\u00a0\n\nNice to Have\n\nExperience supporting customers or acting in a technical support \/ application engineering role.\nKnowledge of automotive AI standards or ADAS perception pipelines.\nExperience contributing to internal tools, scripts, or documentation.\nAbility to read and debug ONNX graphs or intermediate representations.\n\n\nAdditional Information\n\n\n\n\u30eb\u30cd\u30b5\u30b9\u306f\u3001\u300cTo Make Our Lives Easier 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