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Open nowPosted 71 days ago

校招-Physical AI安全研发工程师

nio/campus870 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now校招-Physical AI安全研发工程师nio/campus · Shanghai, Chinese Mainland

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11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
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  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 71 days ago

The posting

1.车载 AI 安全业务模型开发 面向智能辅助驾驶、智能座舱、车控等车载 AI 场景，开发和部署服务于安全业务的车端模型，包括数据采集、模型训练与量化、风险识别和安全策略闭环。 2.车载AI模型与Agent实时监控算法与软件开发 围绕 AI 模型和智能体 Agent 的运行过程，设计并实现异常监控逻辑，识别低置信度输出、输入分布漂移、模型误判、Agent 行为异常、任务执行偏离预期等风险。 3.车载多Agent协同安全机制设计与开发 分析多个 Agent 在感知、决策、交互、控制等链路中的协同关系，识别目标冲突、策略冲突、信息不一致、任务优先级冲突等问题，并设计冲突检测、仲裁和降级处理机制。 4.AI 安全评估体系优化 基于真实数据，生成式AI仿真与对抗攻击等手段，优化车载AI模型与Agent的安全评估体系，内容包含但不限于：安全评估工程框架优化，安全评估维度与指标体系迭代，安全评估算法设计与实现。

1. 具备 AI 模型基础和工程落地能力 熟悉 Python 和 PyTorch / TensorFlow 等深度学习框架，理解目标检测、语义分割、轨迹预测、多模态模型或大模型等至少一种 AI 模型的基本原理，了解模型训练、评估、部署和迭代流程。 2. 具备 Agent 或智能系统理解能力 理解智能体 Agent 的基本工作方式，了解任务规划、工具调用、多模态交互、决策链路或多 Agent 协同中的至少一个方向，对 Agent 行为异常、目标冲突和安全边界问题有兴趣。 3. 具备 AI 安全评估和异常分析能力 理解模型置信度、OOD 检测、不确定性评估、鲁棒性测试、异常输入检测、可解释性分析等 AI 安全相关概念，能够围绕模型和 Agent 失效模式设计测试方法、评价指标和分析流程。 4. 具备软件开发和数据与仿真分析能力 熟悉 Python，具备一定 C/C++、Linux 或嵌入式系统基础，能够完成数据处理、自动化测试、工具开发和实验分析；了解 ONNX Runtime、TensorRT、模型服务化、车载软件或仿真测试经验者优先。

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