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Open nowPosted 114 days ago

具身数据与Agent工程师实习生

anker-in/campushirecn495 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now具身数据与Agent工程师实习生anker-in/campushirecn · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. anker-in/campushirecn postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 114 days ago

anker-in/campushirecn median: 1 days open

The posting

我们正在寻找一位对具身智能（Embodied AI）数据有着浓厚兴趣与极高敏感度的研发实习生。 您将在资深研究员和工程师的指导下，深入剖析机器人操作数据（如遥操作演示、模仿学习数据集）的底层特性，参与构建基于多模态大模型的AI Agent体系。 重点参与设计与实现Agent驱动的数据标注与评估系统，将大模型能力引入机器人数据处理流程，实现从“人工标注”为主到“Agent自动标注 + 人类校验”的范式升级。 通过协助打通“数据流转 → Agent智能标注与质检 → 自动反馈优化”的全链路，研究数据特性并落地具身数据闭环，为各类端到端具身模型（如VLA）的训练提供高质量、大规模的数据基座。

岗位职责： Agent模块开发与智能质检： 参与基于多模态大模型的AI Agent开发，对多视角视频流、语言指令、动作状态序列进行语义对齐与事件切分（temporal segmentation），协助对机器人操作数据（多视角视频流、动作等）进行智能化解析与质量打分。 具身数据特性分析： 参与研究模仿学习等场景下的数据特性。针对具体的前端采集方案（如UMI等），协助分析空间坐标系、动作离散化标准及多源传感器数据的同步要求，配合沉淀具身数据的质检标准。 数据闭环逻辑实现： 协助开发多场景下机器人遥操作采集员的激励支付（Incentive Payment）业务代码。参与编写“前端采集 → Agent判定 → 收益结算”闭环链路中的核心逻辑，并协助落实基础的风控与防作弊策略。 数据复盘与看板搭建： 协助编写数据清洗与统计脚本，参与搭建数据分析仪表盘（Dashboard），跟踪并复盘海量数据底座的采集效率与Agent评估准确率。

任职要求： AI与Agent基础： 对多模态大模型（Multi-modal LLM）有强烈兴趣或使用经验，了解或使用过LangChain等Agent框架 编程与工程能力： 具备扎实的Python编程基础，代码规范良好；熟悉或愿意快速学习多种数据格式（如webdataset、lerobot等）的解析与批处理流转。 具身智能认知： 了解机器人数据基础特性，对模仿学习（Imitation Learning）、Diffusion Policy等技术栈对数据质量的要求有一定认知，对探索并解析复杂的时序动作或视频流数据充满求知欲。 数据处理能力： 掌握SQL，熟悉Pandas、Matplotlib等数据处理与可视化库，能独立完成基础的数据看板搭建。 加分项： 有过大语言模型Agent项目实战经验 了解UMI等具身智能前沿数据采集方案，或有实际的机器人遥操作数据清洗经验

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