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Open nowPosted 97 days ago

蔚来AGI超星计划-AI 原生产品研究员

nio/campus867 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now蔚来AGI超星计划-AI 原生产品研究员nio/campus · Shanghai, Chinese Mainland
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The clock on this job

Early applications get read.

11.5% 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. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 97 days ago

nio/campus median: 1 days open

The posting

方向:ContextOS / Enterprise Agent / Trust & Learning 你要解决的问题 传统产品经理更多是在已有产品范式中定义功能。 这个岗位面对的问题是:产品范式本身还不存在。 你需要参与回答: - 企业 AI 应该如何理解业务对象,而不是直接面对表、字段和文档? - Ontology、Semantic Layer、Evidence 和 Memory 应该如何被业务用户管理和消费? - 用户如何判断一个 AI 结论是否可信、完整、可执行? - Agent 的构建入口会被 Coding Agent 替代后,平台产品还应该提供什么? - tracing 和 badcase 如何变成可治理、可持续进化的企业资产? - Web、SDK、CLI、API 和 Agent Runtime 应该如何共同构成一个完整产品? 你会参与的工作 1. 定义 ContextOS、TrustOps、LearningOps 等新产品的核心模型、用户角色和产品边界; 2. 深入研发、供应链、制造、质量、财务和经营场景,抽象跨领域共性问题; 3. 设计 Ontology Studio、Semantic Mapping Studio、Context Playground、Evaluation Center、Trace & Feedback Studio 等核心产品; 4. 设计从原始资产接入、语义映射、上下文召回、Agent 消费到反馈回流的完整链路; 5. 建立企业 AI 产品的评价体系,不只看 DAU,而是关注任务成功率、证据完整性、资产复用率和业务闭环率; 6. 与算法、架构、工程和业务专家共同完成从研究假设到生产系统的验证。 值得研究的问题 你可能参与形成以下方向的产品研究或论文成果: - 面向企业 Ontology 构建的人机协同交互机制; - AI 结论的证据表达、信任校准和可解释交互; - 面向复杂任务的 Context Engineering 产品范式; - Agent 从建议到执行过程中的 Human-in-the-loop 设计; - 基于 badcase 的企业知识资产持续治理机制; - Coding Agent 时代,企业 AI 平台的产品形态演进; - 面向专家与普通用户的渐进式语义建模体验。 对于适合脱敏和公开的问题,我们支持形成论文、专利、行业方法论或开源项目。

我们希望你 - 来自计算机、人工智能、信息系统、工业工程、设计、人机交互或相关专业; - 能快速理解复杂系统,而不只是拆页面和写需求文档; - 对 AI 产品有真实实践,能够区分模型能力、工程能力和产品能力; - 具备较强的抽象能力,能够从一个业务场景中提炼可复用的平台模型; - 能与工程师和算法研究者深入讨论技术方案,而不是停留在功能描述; - 有独立判断,不迷信行业热词,也不满足于复制已有产品。 加分项包括: - 有 AI Agent、RAG、知识图谱、Semantic Layer、数据平台或开发者工具经验; - 有科研、创业、开源项目或复杂产品从零到一经历; - 能熟练使用 Claude Code、Codex、Cursor 等 Coding Agent 构建原型。

我们能提供什么 1. 真实而复杂的问题 你面对的不是公开数据集上的玩具任务,而是企业研发、供应链、制造、质量、财务和经营中的真实问题。 这些问题同时具备: - 数据规模大; - 资产类型复杂; - 权限约束严格; - 业务口径真实; - 错误成本高; - 研究价值和工程价值并存。 2. 从研究到生产的完整闭环 很多研究止步于离线指标,很多工程止步于功能上线。 在这里,你可以经历: 提出问题 → 建立数据集与基线 → 设计算法或系统 → 进入生产场景 → 收集 tracing 和 badcase → 验证真实业务价值 → 继续迭代 3. 足够大的个人责任边界 我们不希望优秀学生只承担边缘模块。 表现优秀的人可以直接负责: - 一项关键算法方向; - 一个核心产品模块; - 一条端到端技术链路; - 一个跨领域研究课题; - 一套公开 benchmark 或方法论。 4. 研究与公开成果机会 在满足企业安全和数据合规的前提下,我们支持将有普遍价值的问题沉淀为: - 学术论文; - 技术专利; - 公开 benchmark; - 开源框架或工具; - 高质量技术报告; - 行业标准和方法论。 我们不会为了论文而制造问题,但也不会让真正有研究价值的问题只停留在内部项目中。

我们寻找什么样的人 我们不只寻找履历最整齐的人。 我们更看重: - 是否能面对没有标准答案的问题; - 是否能快速进入陌生领域; - 是否有独立判断和研究品味; - 是否能把抽象想法变成可验证的系统; - 是否愿意对最终结果负责; - 是否既追求技术先进性,也理解真实业务约束。 AGI 超星计划不是为了招募更多执行者,而是寻找未来能够定义产品、定义系统、定义算法方向的人。

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