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Open nowPosted 33 days ago

UMI数据Pipeline工程师 / 数据科学家

wcn7iq2vq1am/86371186 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowUMI数据Pipeline工程师 / 数据科学家wcn7iq2vq1am/863711 · Shanghai, 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. wcn7iq2vq1am/863711 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. 58.1%60 days
This job: posted 33 days ago

wcn7iq2vq1am/863711 median: 1 days open

The posting

我们沿用 UMI（Universal Manipulation Interface）范式——用手持夹爪采集人类操作示范——但采集端是自研设备：每只手一组多目相机、IMU 与电动夹爪，配合光学动捕给出位姿，双手同步录制，用于训练操作策略模型。

这个岗位负责这套数据从采集回收到进入训练的整条链路：录后校验、时间基统一、标定应用、多传感器融合、逆运动学解算、格式化与验证、转换为训练布局并交付，以及训练侧反馈的回流。你不需要训练模型，但要清楚训练侧需要什么样的数据，以及链路上任何一环出了问题该去哪里找。

岗位职责 搭建与维护数据处理链路。维护从原始录制到对齐数据的处理管线：多路视频与逐帧时间戳、IMU、夹爪编码、动捕位姿的命名规范化与时间基统一（多时钟对齐）、相机内外参应用与去畸变、IMU 与动捕融合、裁剪 / 对齐 / 平滑、逆运动学解算关节序列，产出 episode 级对齐数据与叠加可视化；管线出问题时定位到具体环节并修复。

录后校验与质量检查。维护采集侧校验规则（各路帧数与速率、硬件同步、序列缺口、夹爪开度域、动捕有效率与残差、各流重叠时长）与格式化后的验证报告；按批次统计可用样本率、返工率，把问题回溯到采集端、标定或设备。

训练格式转换与交付。把格式化产物转换为训练侧约定的数据集布局与字段规范（含元信息、增广信息、来源溯源），管理版本与切分，保证可复现；负责对象存储上的上传、下载与完整性校验。

对接训练侧，形成闭环。了解模仿学习 / VLA 类模型对输入数据的要求，收集训练侧对数据质量、分布、数量的反馈，把改进落实到处理链路、校验阈值与采集 SOP。

标定与配置管理。维护每台设备的配置快照（相机标定、相机到夹爪末端的外参、动捕刚体定义、任务配置）及其版本，参与标定复核与重投影 / 一致性指标评估。

数据资产与存储管理。管理原始数据与处理产物在对象存储和本地阵列上的归档与检索，维护数据台账与血缘，让任何一个训练集都能追溯到原始录制批次与设备。

数据分析、工具与文档。对数据规模、质量、任务与场景分布做统计与可视化，为采集计划与训练实验提供依据；用脚本与自动化工具提升处理吞吐，编写管线文档，让采集与运维同事能自助跑通常规流程。

成长：完整拥有一条从采集到训练的真实数据链路，与算法、采集、硬件团队直接协作；可向数据平台架构、数据闭环算法、具身算法等方向发展。

社招：硕士及以上学历，计算机、机器人、自动化、数学等相关专业，约 3 年数据工程、机器人数据处理或相关经验。

校招：应届硕士，实验室经验须高度对口——做过机器人操作数据采集与处理、模仿学习、多传感器融合或动捕 / SLAM 数据处理中的至少一项；接受长期实习，表现合格可转正。

Python 扎实，熟悉 NumPy / pandas / SciPy，处理过 GB 到 TB 级的多模态时序数据（多路视频、IMU、位姿、夹爪状态）；了解 zarr / HDF5 / parquet 等存储格式。

清楚数据链路的基本功：多时钟时间同步、坐标系与外参、相机标定与去畸变、插值与重采样、滤波与平滑，知道每一步的输入输出是什么；训练侧反馈效果不好时，能追溯到具体的数据分布、处理环节或质量缺陷。

对模仿学习 / VLA 训练数据的输入输出形式有基本了解即可，不要求训练模型。

熟悉 Linux 与 Git，能写可复现的批处理脚本，会把校验与质检结果写成别人看得懂的报告。

对数据敏感、细致，遇到「数据为什么坏了」有刨根问底的习惯；能与采集、运维、算法三方顺畅沟通。

加分项 做过 IMU 与动捕 / 视觉里程计的融合、EKF 或样条平滑，或用 URDF 做过逆运动学求解。 做过光学动捕系统的刚体标定与数据处理，或相机内参 / 手眼外参标定。 熟悉 UMI 官方数据管线（SLAM、replay buffer）或 Diffusion Policy / LeRobot 等训练框架的数据格式。 有数据闭环、自动标注或 Agent 化数据处理工具的经验。 熟悉对象存储（S3 / OSS）、数据版本管理工具（DVC 等）、并行处理框架（Dask / Ray）。 有 ROS / ROS 2 或真机数据经验，熟悉 rosbag / MCAP 等机器人数据格式。 有机器人学习、多传感器融合、三维视觉方向的顶会论文或开源项目。

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