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Open nowPosted 21 hours ago

智能座舱语音测试工程师（资深）

nio/index1,751 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now智能座舱语音测试工程师（资深）nio/index · 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/index's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

12.7% of postings close within 7 days. Measured by our own scanner across the market. nio/index postings stay open a median of 6 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.5%3 days
  3. 12.7%7 days
  4. 20.4%14 days
  5. 35.6%30 days
  6. 58.7%60 days
This job: posted 21 hours ago

nio/index median: 6 days open

The posting

岗位职责 负责座舱语音全链路功能测试，覆盖 ECNR 降噪、唤醒、ASR、NLU/LLM 语义理解、DM 对话管理、TTS 合成，以及多音区对话、全时免唤醒、打断与纠正、拒识等交互能力； 设计并执行语音效果评测，按环节做错误定位与归因：唤醒（漏唤醒、误唤醒）、识别（误识别）、语义（落域错误、答非所问）、合成（自然度、情感表达、多语种）。按版本输出量化结论，判断「体验是否变差」； 建设并迭代测试语料库与评测集：合成语料生成、噪声注入、多场景/多角色/多方言模拟、真实工况采集回灌，沉淀可复用的场景库和用例资产； 针对端云结合与纯端侧两种架构制定不同的测试策略：端云任务切分边界、链路时延分解、弱网/断网/云端降级与兜底、端侧算力/内存/温升约束； 在台架和实车环境执行验证。台架侧：功能回归、链路抓包、问题复现；实车侧：静态与动态路测、多车型/多配置矩阵验证，覆盖真实噪声、车速和多乘员工况； 借助 AI 辅助编程（Vibe Coding）自建测试工具（语料批量回放、结果自动比对、日志解析聚类、评测报告生成），独立完成专项测试（弱网降级、多音区干扰、噪声工况、模型基座切换回归）从方案、工具化到结论的闭环，推动自动化测试接入 CI/CD。

任职要求 本科及以上学历，计算机、人工智能、语音信号处理、电子信息等相关专业，5 年以上车载语音测试经验； 熟悉座舱语音全链路架构，理解端云结合与纯端侧的模块分工差异，能对端到端时延做链路分解，理解端侧推理的算力、内存、功耗约束； 熟悉核心评测指标：唤醒率、24h 误唤醒次数、CER/WER、句准率、语义准确率、首字/端到端响应时延、MOS；具备评测集构建、指标定义和误差根因分析能力； 有台架与实车测试经验：熟悉 HIL/台架搭建，掌握 CANoe/CANalyzer、ADB、日志抓取和网络抓包；有实车静态/动态路测经验，持 C1 及以上驾照，驾龄 2 年以上； 熟练使用 Python，熟悉 Shell/Linux，有自动化测试框架（如 pytest）实践经验； 能熟练借助 AI 编码工具独立开发工具和脚本，对 AI 生成的代码有校验意识（评审、单测、结果抽检），能保证工具可靠。 加分项 有语音大模型效果评测经验（效果回归、幻觉/拒识评估、LLM-as-judge）； 有纯端侧语音方案（离线唤醒、离线识别、端侧大模型）的量产项目经验； 有语音、视觉、车控融合的多模态交互测试经验。

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