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Open nowPosted 5 hours agoWe saw it 65 min after it went up

具身智能数据工程师

agirobot/socialrecruitment484 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now具身智能数据工程师agirobot/socialrecruitment · 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 agirobot/socialrecruitment'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.

13.3% of postings close within 7 days. Measured by our own scanner across the market. agirobot/socialrecruitment postings stay open a median of 6 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.6%3 days
  3. 13.3%7 days
  4. 21.3%14 days
  5. 35.7%30 days
  6. 59.1%60 days
This job: posted 5 hours ago

agirobot/socialrecruitment median: 6 days open

The posting

岗位介绍 面向双足人形机器人的移动操作，参与搭建数据基础设施，接入真机遥操作、 Ego、UMI 等数据，打通从原始数据到模型训练的数据链路。负责数据处理工具、存储检索及版本管理等工程开发，与算法和机器人软件团队协作，提高数据使用效率与训练实验的可复现性。

岗位职责 1. 数据接入与规范：接入真机、遥操作、Ego、UMI 等数据，整理视频、语言、状态、动作及任务结果，建立统一的数据目录、命名、格式与元数据规范。 2. 数据处理流水线：开发数据解析、清洗、切分、同步检查、格式转换、标注、重映射等功能，将原始采集数据转化为可用于训练的数据集，并支持任务重跑、异常记录和结果检查。 3. 存储与检索工具：搭建数据存储、索引、查询及可视化工具，支持按任务、场景、采集批次和质量标签筛选数据，方便算法团队查看、定位和使用样本。 4. 版本与血缘管理：记录数据来源、处理代码与参数、标注版本及数据集发布记录，关联原始数据、派生样本与训练实验，支持问题追溯和训练数据复现。 5. 训练数据对接：与算法团队协作，完成数据格式适配、训练集划分和数据加载接口，支持数据量、质量及分布统计，减少数据准备和使用中的重复工作。 6. 链路维护与迭代：跟进采集、处理与训练中的实际问题，完善基础测试、日志、文档和自动化工具，随数据量和业务需求增长逐步优化系统。

任职要求 1. 计算机、软件工程、人工智能或相关专业本科及以上学历，具有数据工程、后端开发或机器学习工程经验。 2. 熟练掌握 Python，熟悉 Linux、Git，能够独立开发和维护数据处理脚本、工具或服务。 3. 熟悉数据库、文件存储或对象存储的基本使用，能够设计合理的数据结构、索引与接口。 4. 具有图像、视频、时序数据或机器学习数据集处理经验，能够完成数据校验、格式转换及问题排查。 5. 理解数据版本、元数据与处理记录的重要性，具备良好的工程习惯，重视代码可维护性和结果可复现。 6. 能够与算法、采集及机器人软件团队沟通，将实际需求转化为可用的工程工具。

加分项 - 有从 0 到 1 搭建数据处理链路、训练数据工具或内部数据平台的经验。 - 有 Ego、UMI、遥操作或真机运行数据处理经验，熟悉 ROS/ROS2、MCAP、LeRobot 或 RLDS。 - 有数据版本管理、血缘追踪、数据标注平台或质量检查工具的开发经验。 - 有 Ray、Spark 等分布式处理框架使用经验，或参与过大规模数据处理、任务调度及性能优化。 - 有 GPU 批量推理、自动标注、语义去重或训练数据加载优化经验。

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