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发布于 2026-09-02 英文原文

AI应用工程师

Application Engineer

ルネサス エレクトロニクス

职种工程 雇用形态—
工作地点Kodaira, jp 薪资面议

职位概要

该职位为AI应用工程师,负责在汽车级SoC上支持AI模型的启用、优化与部署。主要职责包括:在Gen4/5 SoC平台(CNNIP/DSP/NPU HWA)上启用和部署BEV、目标检测、分割、分类等AI模型;进行模型性能分析(延迟、吞吐量、多核扩展),识别内存带宽、调度或算子映射瓶颈;支持PTQ、QAT协作、算子融合、图优化和执行分区等优化流程;分析量化或算子限制导致的精度下降并提出缓解策略。需将AI模型集成到Linux/QNX嵌入式运行时环境,调试CNNIP/DSP/NPU卸载、内存分配/IPMMU、数据传输开销和多核同步问题,并在目标板和仿真器(SIL/HIL)上验证AI工作负载。还需使用AI编译器和运行时工具链(ONNX工作流、混合编译器、MWMX),支持ONNX模型处理(图检查与修改、模型分割与执行控制、QDQ ONNX模型),开发维护内部工具和脚本。作为客户、内部开发团队和现场应用工程师之间的技术接口,支持客户评估、PoC和演示,提供技术指导、文档和最佳实践。要求计算机科学、电气工程、嵌入式系统学士或硕士学位或相关经验;扎实的深度学习基础和推理流水线理解;熟悉PyTorch、ONNX或ONNX Runtime;精通Python,了解C/C++更佳;熟悉嵌入式系统和调试工具;能使用延迟、吞吐量和硬件利用率等指标分析性能;具备多文化跨职能环境的良好沟通能力。优先1-3年嵌入式系统或AI开发经验,熟悉计算机视觉模型、汽车或机器人用例、嵌入式硬件AI推理优化、量化技术(INT8、校准方法、QDQ模型)、汽车SoC或QNX安全软件环境、SoC架构内存层次/DMA/多核调度。公司为全球嵌入式半导体解决方案领导者,在30多个国家拥有超过21000名工程师,以TAGIE为企业文化核心,提供有竞争力的薪酬和福利。

职位描述

Job Description Job Summary We are looking for an AI Application Engineer to support the enablement, optimization, and deployment of AI models on automotive-grade SoCs. In 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. Key Responsibilities AI Model Enablement & Optimization Enable and deploy AI models (e.g., BEV, object detection, segmentation, classification) on Gen4/5 SoC platforms with CNNIP/DSP/NPU HWA. Perform model performance analysis (latency, throughput, multi-core scaling) and identify bottlenecks related to memory bandwidth, scheduling, or operator mapping. Support model optimization workflows, including: Post-Training Quantization (PTQ) Quantization-Aware Training (QAT) collaboration Operator fusion, graph optimization, and execution partitioning Analyze accuracy degradation caused by quantization or operator limitations and propose mitigation strategies. Embedded AI Inference & System Integration Integrate AI models into embedded runtime environments (Linux / QNX). Debug issues related to: CNNIP/DSP/NPU offloading Memory allocation / IPMMU Data transfer overhead and multi-core synchronization Validate AI workloads on target boards and simulators (SIL / HIL). Toolchain & Model Workflow Support Work with AI compiler and runtime toolchains (e.g., ONNX-based workflows, hybrid compiler, MWMX). Support ONNX model handling, including: Graph inspection and modification Model segmentation and execution control Quantized (QDQ) ONNX models Develop or maintain internal tools and scripts to improve model validation, benchmarking, and customer workflows. Customer & Cross-Team Collaboration Act as a technical interface between customers, internal development teams, and field application engineers. Support customer evaluations, PoCs, and demos on automotive AI platforms. Provide technical guidance, documentation, and best practices for AI model deployment. Contribute to weekly technical reports, issue tracking, and release validation activities. Qualifications Required Qualifications Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Embedded Systems, or have experience in embedded systems. Solid understanding of deep learning fundamentals and inference pipelines. Hands-on experience with AI frameworks such as PyTorch, ONNX, or ONNX Runtime. Strong programming skills in Python; working knowledge of C/C++ is a plus. Familiarity with embedded systems and debugging tools. Ability to analyze performance using metrics such as latency, throughput, and hardware utilization. Good communication skills in a multi-cultural, cross-functional environment. Preferred / Optional Qualifications 1–3 years of experience in embedded systems or AI-related development. Experience with AI model training, fine-tuning, or evaluation, especially for: Computer vision models (Detection / Segmentation / BEV) Automotive or robotics use cases Practical experience with AI inference optimization on embedded hardware (NPU, DSP, GPU, or CPU). Familiarity with quantization techniques (INT8, calibration methods, QDQ models). Experience with automotive SoCs or safety-related software environments (QNX is a plus). Understanding of memory hierarchy, DMA, and multi-core scheduling in SoC architectures. Nice to Have Experience supporting customers or acting in a technical support / application engineering role. Knowledge of automotive AI standards or ADAS perception pipelines. Experience contributing to internal tools, scripts, or documentation. Ability to read and debug ONNX graphs or intermediate representations. Additional Information ルネサスは、「To Make Our Lives Easier(人々の暮らしをより豊かで快適にする)」というPurposeのもと、組込み半導体ソリューションを提供するグローバル企業です。世界30か国以上で活躍する21,000人を超えるエンジニアや課題解決のプロフェッショナルとともに、自動車、産業、インフラ、IoT分野における世界最先端のテクノロジー開発に携わり、より安全で、健康的で、環境にやさしく、スマートな未来の実現に貢献しています。 ルネサスでは、「TAGIE(Transparent、Agile、Global、Innovative、Entrepreneurial)」を企業文化の中核としています。TAGIEは、私たちの働き方や成長のあり方、そしてPurposeの実現に向けた取り組みを支える共通の価値観です。この協調的な精神と挑戦するマインドセットが、半導体技術を通じた産業の変革と、世界中の人々の暮らしへの貢献を可能にしています。 私たちは、競争力のある報酬制度に加え、充実した福利厚生をご用意しています。福利厚生の詳細については、選考プロセスの中でご案内いたします。 私たちとともに未来を創造する挑戦に、ぜひ参加しませんか。皆さまからのご応募をお待ちしております。