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Open nowPosted 97 days ago

蔚来AGI超星计划-企业智能系统研发工程师

nio/campus867 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now蔚来AGI超星计划-企业智能系统研发工程师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 / Agent Runtime / Distributed AI Systems 你要解决的问题 企业 AI 的困难，不只是调用一次模型。 它需要在复杂权限、异构数据、长链路任务和真实生产环境中稳定运行。 你需要参与建设： - 能连接企业数据、知识、代码、API 和日志的上下文基础设施； - 能供不同 Agent 和 Coding Agent 调用的统一 Runtime； - 能追踪每一次上下文召回、推理和工具执行的运行系统； - 能在权限、安全、审计和成本约束下完成复杂任务的执行框架； - 能支撑企业级 Ontology、Semantic Mapping 和 Context Assemble 的核心引擎。 你会参与的工作 1. 设计和实现 ContextOS 的核心服务，包括对象模型、语义映射、上下文组装、证据链和企业记忆； 2. 建设面向数据表、文档、代码、API、图谱和日志的统一资产接入及处理框架； 3. 设计 Ontology 驱动的查询规划、联邦检索和事实组装机制； 4. 建设 Agent Runtime SDK、Skill Gateway、MCP Gateway、Sandbox 和 Workflow Engine； 5. 打通用户权限、数据权限、工具权限、动作权限和审计链路； 6. 建设 tracing、replay、evaluation hook、badcase 回流和版本回归能力； 7. 面向真实业务场景优化系统性能、稳定性、成本和开发者体验。 值得研究的问题 - 面向企业异构资产的统一语义访问协议； - Ontology 驱动的多源查询规划和执行优化； - 结构化数据、非结构化知识、图谱与 API 的联合 Context Assemble； - 长任务 Agent Runtime 的状态管理、恢复和可回放机制； - 复杂业务 API 的参数规划、约束满足和可靠执行； - 企业级 Agent 的权限传递、最小授权和动态风险控制； - Context Cache、增量更新和时效性一致性； - 多 Agent / 多 Skill 场景下的调度和协同机制； - 面向 Coding Agent 的企业级 Harness 与运行时标准。

我们希望你 - 来自计算机、软件工程、分布式系统、数据库或相关专业； - 具备扎实的计算机基础，理解操作系统、网络、数据库和分布式系统； - 至少熟练掌握 Python、Java、Go、Rust 中的一种； - 能够独立设计并完成一个可运行的系统，而不只是调用已有框架； - 对工程质量敏感，重视测试、可观测性、边界条件和失败恢复； - 对 Agent、RAG、Ontology、数据库或检索系统中的至少一个方向有深入实践。 加分项包括： - 参与过数据库、搜索引擎、数据湖、图数据库、编译器或工作流系统建设； - 熟悉 Spark、Flink、Trino、Iceberg、Doris、Neo4j、Elasticsearch 等系统； - 熟悉 LangGraph、DeepAgents、Semantic Kernel、LlamaIndex 等框架，但不被框架限制； - 有开源贡献、系统论文、程序设计竞赛或高质量个人项目。

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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