快手广告算法工程师-【海外商业化】
社招全职D11917地点:北京状态:招聘
任职要求
1、动手能力强,熟悉C/C++/Python开发,熟悉Hive、Spark等大数据处理框架; 2、熟悉常用的机器学习和数据挖掘算法,有扎实的数学基础,善于从数据中发现、分析和解决问题; 3、善于阅读文献,快速学习,具备优秀的分析和解决问题的能力,良好的沟通协作能力; 4、有大规模计算广告、推荐系统、搜索引擎、风控系统、电商系统等经验者优先。
工作职责
1、参与快手海外广告系统的核心机制策略、模型算法的研究及开发工作,服务不同国家和地区的用户,助力快手海外广告业务快速增长; 2、设计和实现高效的广告检索和排序算法,运用运筹优化、Uplift 建模 / 因果推断、参数模型化等技术和理论,探索给定资源约束下最大化流量和生态长短期价值的最佳机制策略实践; 3、研发业界领先的智能出价系统和智能投放平台,通过应用控制理论、强化学习等前沿技术,提升广告主投放的长短期收益; 4、优化广告的点击率和转化率,从模型结构设计、训练范式优化、用户特征挖掘、转化延迟建模等角度提升模型预估精准度。
包括英文材料
C+
https://www.freecodecamp.org/chinese/news/the-c-beginners-handbook/
本手册遵循二八定律。你将在 20% 的时间内学习 80% 的 C 编程语言。
https://www.youtube.com/watch?v=87SH2Cn0s9A
https://www.youtube.com/watch?v=KJgsSFOSQv0
This course will give you a full introduction into all of the core concepts in the C programming language.
https://www.youtube.com/watch?v=PaPN51Mm5qQ
In this complete C programming course, Dr. Charles Severance (aka Dr. Chuck) will help you understand computer architecture and low-level programming with the help of the classic C Programming language book written by Brian Kernighan and Dennis Ritchie.
C+++
https://www.learncpp.com/
LearnCpp.com is a free website devoted to teaching you how to program in modern C++.
https://www.youtube.com/watch?v=ZzaPdXTrSb8
Python+
https://liaoxuefeng.com/books/python/introduction/index.html
中文,免费,零起点,完整示例,基于最新的Python 3版本。
https://www.learnpython.org/
a free interactive Python tutorial for people who want to learn Python, fast.
https://www.youtube.com/watch?v=K5KVEU3aaeQ
Master Python from scratch 🚀 No fluff—just clear, practical coding skills to kickstart your journey!
https://www.youtube.com/watch?v=rfscVS0vtbw
This course will give you a full introduction into all of the core concepts in python.
Hive+
[英文] Hive Tutorial
https://www.tutorialspoint.com/hive/index.htm
Hive is a data warehouse infrastructure tool to process structured data in Hadoop. It resides on top of Hadoop to summarize Big Data, and makes querying and analyzing easy.
https://www.youtube.com/watch?v=D4HqQ8-Ja9Y
Spark+
[英文] Learning Spark Book
https://pages.databricks.com/rs/094-YMS-629/images/LearningSpark2.0.pdf
This new edition has been updated to reflect Apache Spark’s evolution through Spark 2.x and Spark 3.0, including its expanded ecosystem of built-in and external data sources, machine learning, and streaming technologies with which Spark is tightly integrated.
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
数据挖掘+
https://www.youtube.com/watch?v=-bSkREem8dM
Database vs Data Warehouse vs Data Lake
https://www.youtube.com/watch?v=7rs0i-9nOjo
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
推荐系统+
[英文] Recommender Systems
https://www.d2l.ai/chapter_recommender-systems/index.html
Recommender systems are widely employed in industry and are ubiquitous in our daily lives.
相关职位
社招D11917
1、参与快手海外联盟广告系统的算法研发与策略优化,涵盖流量定价、广告匹配、收益分成等关键链路,服务全球开发者和广告主,驱动联盟广告业务增长; 2、设计并优化联盟广告流量的投放和分发机制,结合因果推断、博弈论、运筹优化等理论,提升平台收益与开发者体验之间的平衡; 3、研发适用于联盟生态的流量匹配模型,提升CTR/CVR/竞胜率等关键指标的准确性,兼顾kwai、非kwai泛化迁移能力; 4、探索跨平台、跨形态广告生态中的新型匹配与变现策略,推进联盟广告系统的稳定性、鲁棒性与智能化算力; 5、探索LLM在联盟生态的价值,通过LLM的世界知识和推理能力缓解联盟的数据缺乏问题。
更新于 2025-07-14
社招5年以上A126418A
国际化搜索广告团队不断突破通用搜索引擎变现的界限,覆盖海外应用,致力于构建全球领先的搜索广告变现系统。在国际化搜索广告团队,您将有机会从事大规模分布式存储和架构、自然语言处理、排序和信息检索相关的问题。您还将深入参与我们的广告样式、创意展示和广告投放链路的创新和优化。我们正在寻找勇于挑战困难、热衷于解决复杂问题并与热情洋溢的候选人们共同发展我们的搜索广告产品。 1、参与大规模广告系统的开发; 2、使用机器学习参与广告投放的开发和迭代,参与点击率/转化率模型估计准确性、数据分析、建模、特征工程; 3、参与自然语言处理 (NLP) 能力的提升和查询理解,例如查询分类、seq2seq、实体识别 (NER)、知识图谱、关键词优化等; 4、负责相关性模型和策略优化,例如语义匹配模型、主动学习、文本/照片/视频多模型、排序策略等; 5、 研究和开发广告定向、出价算法、广告流量控制等; 6、 与产品经理和产品战略与运营团队合作,定义产品策略和功能。
更新于 2023-04-19