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Open nowPosted 102 days ago

推荐算法工程师

kaonai/index37 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now推荐算法工程师kaonai/index · Beijing, Chinese Mainland

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Early applications get read.

12.3% 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. 4.5%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 102 days ago

The posting

岗位方向 负责 AI 角色/对话场景下的推荐算法设计、优化与落地：以排序模型与机制为主，协同全链路策略，并探索生成式推荐与现有排序体系的融合，持续提升分发效率、拉动核心业务增长。

1. 算法设计与优化：负责/协同对话推荐场景下的排序模型与排序机制迭代，覆盖多目标/多任务、表征与目标融合等方向；与召回、冷启、探索等模块协同推进跨层优化。 2. 数据驱动迭代：基于用户行为、内容与对话侧特征数据，构建评估与归因方法，通过 A/B 验证算法效果，聚焦匹配与开聊等核心问题持续迭代。 3. 特征与模型建设：参与用户/内容/对话相关特征挖掘与样本体系建设，结合场景选择或改进推荐模型，提升预测精度和泛化能力。 4. 协同落地：与工程、产品、数据等团队协作，将算法方案转化为可上线实现，保障可用性与迭代效率，适配多场景需求变化。 5. 技术探索与沉淀：跟踪推荐算法与大模型在对话/内容领域的进展，探索生成式推荐、对话侧信号与现有排序体系的融合，沉淀可复用的技术方案与实验经验。

1. 学历背景：计算机、软件工程、数学、统计等相关专业本科及以上学历（硕士优先）。 2. 工作经验：2–5 年推荐/搜索/广告算法经验；有对话推荐、角色/UGC 内容平台、短视频/图文等内容分发经验者优先。 3. 技术能力：熟悉常见推荐算法；熟练使用 Python，熟悉 TensorFlow 或 PyTorch；具备 SQL 等数据处理与分析能力。 4. 业务理解：理解推荐业务逻辑，能将用户与场景问题抽象为算法问题并给出可落地方案。 5. 项目经验：有独立负责推荐模块优化，或参与完整链路搭建、核心指标提升的经历，能清晰说明项目成果与个人贡献。

加分项 1. 有多目标精排、表征学习、个性化 uplift 等排序相关落地经验。 2. 具备大模型相关实践，如语义表征、对话理解、query/内容理解，或 LLM×推荐融合探索。 3. 有大规模推荐系统落地经验，熟悉工程优化（如 serving、特征链路、模型压缩、离在线计算架构等）。

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