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Open nowPosted 264 days ago

AIGC图像数据工程师

kaonai/index37 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowAIGC图像数据工程师kaonai/index · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.5%14 days
  5. 35.5%30 days
  6. 45.1%60 days
This job: posted 264 days ago

The posting

  • 负责图像与多模态数据全生命周期建设：围绕业务目标与模型训练需求，搭建数据获取 → 清洗 → 标注/弱标注 → 评估 → 版本管理 → 回流迭代的闭环流程。
  • 参与端到端多模态统一模型的数据体系：图文交错序列、多轮长轨迹、理解 + 生成联合数据、跨帧一致性（角色/场景/光线/风格/世界观）数据的构造、清洗、标注与配比。
  • 数据采集与合规治理：推进数据来源梳理、授权/版权风险排查、敏感内容过滤与安全策略落地，建立可审计的数据资产规范。
  • 清洗与质量提升：实现去重（近重复/语义重复）、质量筛选（清晰度/噪声/美学/主体完整性等）、异常检测（NSFW/水印/低质/违规）与长尾数据治理。
  • 数据标注体系建设：设计并迭代标注 schema（caption、tag、属性、风格、构图、人物/商品信息、图文交错结构、多轮对话轨迹等），制定标注指南与质检规则，推动标注一致性与效率提升。
  • 难例挖掘与数据配比：围绕线上问题（如一致性、可控性、编辑失败、风格偏移、长程一致衰减等）挖掘难例与负例，构建训练集/验证集配比策略与覆盖度分析。
  • 数据工程化与平台化：参与数据管线工程建设（ETL/调度/缓存/存储/索引），支持大规模数据处理与高吞吐读取，提升训练数据供给效率与稳定性。
  • 数据评估与可观测性：建立数据质量指标与报表（分布漂移、覆盖率、噪声率、标注一致性等），支持「数据 → 训练 → 评测」的问题定位。
  • 跨团队协作：与算法/训练工程/产品/运营协作，将业务问题沉淀为可复用的数据策略、工具链与最佳实践文档。
  • 计算机/数学/统计/相关专业本科及以上，具备扎实的数据处理与工程能力。
  • 熟悉 Python 数据处理栈（Pandas / PyArrow / PIL/OpenCV 等），有大规模数据清洗与处理经验。
  • 熟悉至少一种数据/任务调度与存储体系（如 Airflow/Argo、HDFS/S3/OSS、Parquet/LMDB/WebDataset 等）或具备快速上手能力。
  • 了解机器学习/深度学习训练对数据的基本要求：数据分布、噪声、偏差、过拟合、数据泄漏、训练/验证划分等。
  • 具备良好的工程习惯：可复现、规范记录、版本管理、数据血缘追踪、质量监控与故障排查能力。
  • 沟通协作能力强，能把「业务现象」转译成「可落地的数据指标与治理方案」。

加分项 - 有图像生成/多模态数据经验：caption/标签体系、审美与风格数据、编辑/可控数据、对齐偏好数据（pairwise preference）等。 - 有图文交错 / 多轮多模态 / 理解-生成统一数据经验，或对话式长轨迹、跨帧一致性（角色/场景/风格）标注经验者优先。 - 熟悉大规模去重与检索：哈希去重（pHash/aHash/dHash）、向量检索（FAISS/Milvus）、CLIP embedding 分析等。 - 有数据质量自动评估经验：美学打分、主体检测、OCR/水印检测、NSFW/安全分类等。 - 有标注平台/质检体系经验：标注流程设计、抽检策略、IAA（一致性）度量、标注效率优化。 - 熟悉训练数据格式与读取优化：WebDataset、TFRecord、LMDB、mmap、prefetch/cache、数据打包与 shard 策略。

我们能提供 - 直接参与核心业务的 AIGC 数据资产建设，数据策略直接影响线上模型指标。 - 完整的「数据 → 训练 → 评测 → 上线」闭环，配套算力资源与跨团队协作机制。 - 成长空间清晰：数据治理 / 多模态理解-生成统一数据体系 / 偏好数据与对齐 / 数据平台化等方向深入发展。

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