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Open nowPosted 72 days ago

校招-供应链算法工程师

nio/campus868 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now校招-供应链算法工程师nio/campus · Shanghai, Chinese Mainland

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  2. CV RocketCV written for this posting.

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

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The clock on this job

Early applications get read.

11.9% 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. 11.9%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 72 days ago

nio/campus median: 1 days open

The posting

业务背景 在汽车行业智能化与电动化趋势下，供应链已经成为企业核心竞争力之一。一辆汽车涉及上万个零部件及多个业务系统的全球协同，任何一个环节的信息不透明、风险识别不及时或决策滞后，都可能影响生产、库存、交付与成本。 团队正在建设面向新能源汽车供应链的新一代数据与 AI 能力体系，包括供应链数据底座、企业知识体系、Agent 平台与智能决策能力。我们希望通过 Data-driven 的方式推动供应链管理线上化，并结合 AI Co-pilot 与 Agentic AI，打造覆盖“监控、预警、分析、决策、执行”的供应链 Control Tower。 岗位将参与供应链 AI Agent 的设计、开发、评估与落地，服务于采购、计划、物流、库存及供应商管理等核心业务场景。 岗位职责 参与新能源汽车供应链场景 AI Agent 的设计与开发，将缺料分析、供应风险排查、库存诊断等业务问题拆解为可规划、可调用工具、可验证结果的智能任务。 基于 ReAct、Planning、Tool Calling、Memory 等机制，构建具备任务理解、步骤规划、知识检索、工具调用及结果校验能力的 Agent。 参与企业级 Agent Harness / Runtime 建设，包括 Tool / Skill 管理、Context 管理、任务状态管理、执行编排、失败重试、权限控制与运行可观测性等能力。 参与 Agent 评估体系建设，围绕任务成功率、工具调用准确率、知识检索质量、执行效率及结果可信度构建评测集，并通过失败案例分析持续优化系统。 参与供应链知识体系建设，包括 Ontology 建模、知识图谱、GraphRAG 和企业语义体系抽象，提升 Agent 对企业数据、知识及业务关系的理解能力。 与供应链、采购、计划、物流等业务团队，以及数据、产品和研发团队协同，推动 AI 能力在真实业务流程中落地并持续迭代。

岗位要求 本科及以上学历，硕士优先；计算机、人工智能、软件工程、数据科学、自动化、数学、统计学、运筹优化等相关专业优先。 具备扎实的计算机基础与工程能力，熟悉 Python，掌握常用数据结构与算法，具备良好的代码质量和系统设计意识。 理解 Agentic AI 与固定 Workflow Automation 的区别，了解任务规划、工具调用、上下文管理、知识检索、结果校验及长链路任务执行等核心机制。 通过课程、科研、竞赛、实习或个人项目，完成过至少一个包含大模型调用、RAG、Tool Calling 或任务编排的 AI 应用，并能够清晰说明其技术方案、评估方式与局限性。 具备良好的问题拆解和学习能力，能够将复杂业务问题抽象为数据、知识、工具及执行流程。 具备良好的沟通协作能力，对供应链、制造业及企业级 AI 应用有兴趣。 不要求候选人覆盖全部技术方向。具备其中一个方向的深入项目经验，并对其他方向有基本理解即可。 熟悉或接触过以下技术方向者优先 LangGraph、AutoGen、CrewAI、Semantic Kernel、OpenAI SDK、MCP（Model Context Protocol）等 Agent Framework / Runtime。 Prompt / Context Engineering、复杂 Tool Calling、任务状态管理、Agent Evaluation、Tracing 或长链路任务执行。 Ontology、知识图谱、GraphRAG、企业知识组织与语义抽象。 Neo4j、NebulaGraph、Milvus、pgvector、Weaviate 等图数据库或向量数据库。 Embedding、Reranking、Retrieval Pipeline、Text-to-SQL 或 Tool Calling Infrastructure。 API 与数据库集成、异步任务、日志监控、自动化测试或 AI 应用部署。 有真实 AI Agent 项目、开源项目、科研竞赛、企业实习，或供应链、汽车及制造业相关经验。

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