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Kuaishou Technology · Beijing

# 广告算法实习生-【增长投放】

Beijing 

Listed on Kuaishou Technology’s official careers site. Listed as of October 19, 2026. Check that it is still open and apply there.
[Apply on Kuaishou Technology’s careers site →](https://zhaopin.kuaishou.cn/#/official/social/job-info/20325)

[OpenNash Jobs](https://jobs.opennash.com/) / [Kuaishou Technology](https://jobs.opennash.com/companies/kuaishou-technology/) / 广告算法实习生-【增长投放】

## Job description

团队介绍 快手增长算法团队负责通过广告外投驱动快手 APP 的拉新、拉回与拉活。我们不只关注点击和转化，更关注用户生命周期内真实的长期价值——如何在投放侧精准识别"值得投的用户"，并持续优化这个判断，是团队当前最核心的技术命题。 职位描述
- 参与用户长期价值（LTV）建模的研究与工程落地，包括价值预估模型的设计、特征体系建设与模型迭代优化；
- 研究跨域用户行为建模，利用广告侧与产品侧的多源信号，提升对新用户与流失用户价值的预判能力；
- 探索强化学习、因果推断、序列建模等方向在长期价值优化中的应用，突破短期指标局限；
- 参与 CTR/CVR 预估、竞价策略等全链路模型的迭代，围绕 ROI 与 LTV 双目标联合优化；
- 探索大语言模型（LLM）在广告增长场景中的应用，包括但不限于用户意图理解、创意理解优化、多模态特征融合等方向。
- 在读硕士或博士，计算机、统计学、数学或相关专业背景；
- 扎实的机器学习基础，熟悉深度学习框架（PyTorch / TensorFlow），具备独立实验能力；
- 熟练使用 Python 进行数据处理与建模，有 SQL / Spark 使用经验优先；
- 能阅读英文文献，具备将论文思路转化为工程实验的能力。 加分项
- 有用户价值建模、LTV 预估、留存预测等相关研究或实习经验；
- 熟悉因果推断（Uplift Modeling、DID、IV）或强化学习在业务中的应用；
- 有计算广告、推荐系统、用户行为序列建模相关背景；
- 有大模型微调、Prompt Engineering、RAG 或 LLM Agent 相关项目经验；
- 在 KDD、NeurIPS、ICML、WWW、WSDM、RecSys 等顶会有论文发表或在投。

## At a glance
- Posted by the employer: Not stated
- First seen by OpenNash: Oct 19, 2026
- Last checked: Oct 19, 2026
- Listed for: Under a day
- Location: Beijing[Apply on Kuaishou Technology’s careers site →](https://zhaopin.kuaishou.cn/#/official/social/job-info/20325)

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