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Open nowPosted 122 days ago

数据算法开发工程师/专家

agirobot/referral515 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now数据算法开发工程师/专家agirobot/referral · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. agirobot/referral postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 122 days ago

agirobot/referral median: 1 days open

The posting

岗位定位： 面向具身数据工程的数据算法开发，覆盖数据质量评价与价值评估、多模态处理、自动标注、数据质检、数据挖掘等方向。可根据个人专长匹配其中一个或者多个方向。 岗位职责： 1.制定并维护多模态数据的质量规则与标准，涵盖完整性、时序对齐、通道齐全、格式合规、标注质量、多样性等方面。建设数据质量评价指标体系与质量门禁，作为下游质检与入库的统一依据。同时，建立数据 - 模型全链路归因与消融实验，进行数据价值建模与 Scaling 曲线分析，通过数据配比优化反哺采集与数据选择策略。 2.负责多模态原始数据（如视频、传感器、轨迹等，以 rosbag、MCAP 等格式存储）的清洗、解析与预处理工作。建设多传感器时间同步、空间对齐与标定，以及抽帧、点云与视频解码等预处理引擎。统一数据对象与元数据管理（Session、Topic、Channel、Manifest），落实 DatasetVersion、DataTaskRun 口径与 Data Contract。 3.基于 VLM、FM 构建具身智能数据预标注体系，搭建覆盖 2D 图像语义、3D 点云与多模态文本描述的自动化标注流水线。研发在线打标（Online Labeling）与基于置信度的自动初筛，通过难例挖掘突破长尾场景。同时，开发自动化质检与异常检测（包括异常轨迹、坏帧、标定漂移、标注错误等），建设低质数据回流的质量反馈闭环，并依据数据质量评价体系标准执行。 4.负责海量多模态数据的数据选择、去重（场景与轨迹相似度、语义级）与多样性分析，实现长尾 Corner Case 的自动识别、聚类与召回。设计主动学习策略筛选高价值样本，通过数据配比优化反哺数据采集工作。 5.进行文本、语音、对话语料的清洗、去重（MinHash、SimHash、LSH）、质量过滤与配比。完成自然语言理解数据（方言、口语化）的 ASR 转写与意图、槽位、实体标注；开展情感计算数据的文本与语音情感标注、构建情感标签体系与对话情绪流转；进行知识图谱的实体与关系抽取、三元组构建、知识对齐与融合、KG - to - text 及检索增强；负责大模型后训练语料（指令、SFT、偏好与 RLHF 数据）的生产与质量评估。

1.硕士及以上优先，机器学习、数据科学、计算机或机器人等相关专业；3 年以上算法或数据工程经验；精通 Python（算法方向需熟悉 PyTorch）。 2.至少精通下列方向之一即可： 1）质量评价：熟悉数据质量评估、数据选择与配比、Scaling Law 及模型评测方法，具备消融实验设计与量化归因分析能力。 2）多模态处理：熟悉 ROS、ROS2、rosbag、MCAP、FFmpeg、OpenCV、点云处理（PCL），具备 C++、Python 能力及多传感器标定与时间同步经验。 3）自动标注、质检、挖掘：熟悉 2D、3D 计算机视觉与 VLM、异常检测与数据质量，或数据挖掘、主动学习与聚类。 3）交互语料：熟悉语料工程（清洗、去重、质量过滤、配比）、NLP 标注（意图、槽位、NER、情感）、语音与 ASR 数据，或知识图谱抽取，理解大语言模型的数据需求（预训练、SFT、RLHF）。 · 具备独立搭建数据算法或处理闭环的能力与跨方向学习意愿。

加分项 1.有端到端、VLA、大模型的数据-模型闭环经验，熟悉数据混合策略（真机遥操作、无本体人类视频、仿真合成数据的最优配比）与消融分析。 2.有智驾数据闭环全链路（多模态解析、Auto Labeling、DQC、Corner Case 挖掘）的端到端经验。 3.熟悉在线学习与实时流处理、LeRobot、HDF5 等机器人数据格式；处理过 Ego4D、Open X-Embodiment 等大规模具身数据集；有数据质量或数据治理标准制定经验。 4.有大语言模型预训练或后训练语料工程经验；有方言、多语种、低资源语言数据，或知识图谱建设与检索增强（RAG）数据链路经验。

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