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Open nowPosted 73 days ago

校招-车控算法部署和优化工程师

nio/campus869 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now校招-车控算法部署和优化工程师nio/campus · Shanghai, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on nio/campus's own form.

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. nio/campus postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 73 days ago

nio/campus median: 1 days open

The posting

车控边缘AI用例发掘与构建：立足与智能车控领域（智能底盘/智能灯光/舱内外感知/车身运动件规控等），识别并设计适用于边缘端的AI落地场景，场景可贯穿车辆研发阶段、售后车辆、虚拟传感器、以及物理部件的虚拟化/替代等； 跨域协同建模：联合领域专家提炼用例，并将其映射为车辆系统（硬件+软件）的数据、模块与流程表达； 边缘AI全栈交付：基于边缘SoC/加速器（如NPU）及对应工具链的深厚积累，负责Edge AI用例的设计、架构、部署与验证。包括模型格式（ONNX/自定义格式）、量化方式、轻量化推理框架、时序调度、数据处理、强化学习框架、规划实时控制框架在嵌入式侧的运行方案； 持续创新：研究和学习新技术，以满足下一代软件基础设施框架的迭代更新；

教育背景： 学历与专业：研究生及以上学位（博士优先），计算机、软件工程、电子工程、汽车电子等相关专业，扎实的计算机体系结构和数学基础； 优先条件：智能辅助驾驶、具身智能、智能车控或AI模型工程化领域的研究经历；

核心技术能力： 架构认知：扎实的CPU/SoC架构知识，熟悉ML/AI加速器、性能与优化准则，如算子融合、内存优化、计算图调度等底层优化技术； 工程能力：了解从仿真训练到嵌入式部署的工程链路，包括ONNX模型格式转换、量化压缩（INT8/FP16）、推理框架优化（如TensorRT、ONNX Runtime、TVM）等； 算法训练：熟悉强化学习框架设计和优化，如On/Off-Policy并行加速等技术； 编程与工具链：熟练使用Python/C++/JAX，及PyTorch、TensorFlow、Keras、TinyML等工具链，熟悉Linux，掌握实时任务调度、多线程通信主要机制； 综合素质：具备创造性思考习惯，中、英语流利，熟练使用AGI Research/Coding工具；

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