小米高级广告算法工程师
社招全职3年以上A174485地点：北京状态：招聘
任职要求
1、在推荐或广告领域有着3年以上的从业经验，做过大规模广告或推荐系统相关算法，对业务有着深刻理解； 2、扎实的工程能力，有C++或Java线上开发经验； 3、熟悉Pytorch、TensorFlow等深度学习框架； 4、熟练运用Spark、Flink等大数据处理框架； 5、性格乐观积极，不满足现状，勇于突破，充满理想和热情，致力于解决实际问题； 6、优秀的执行力、推动力和沟通能力。
工作职责
1、负责优化小米多个商业化场景的搜索和ocpx广告模型，采用先进的模型技术，提升广告变现效率； 2、采用业界深度学习、迁移学习、强化学习等前沿技术, 将其在商业化多个场景中进行落地应用； 3、分析海量用户广告行为和内容消费行为，增加有效特征，挖掘用户兴趣，优化排序和竞价机制； 4、优化广告系统投放链路、算法和机制策略，提升变现效率，提升客户效果。
包括英文材料
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.
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/
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
Java+
https://www.youtube.com/watch?v=eIrMbAQSU34
Master Java – a must-have language for software development, Android apps, and more! ☕️ This beginner-friendly course takes you from basics to real coding skills.
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具，在灵活性、可读性和性能上都具备相当的优势，近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
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.
Flink+
https://nightlies.apache.org/flink/flink-docs-release-2.0/docs/learn-flink/overview/
This training presents an introduction to Apache Flink that includes just enough to get you started writing scalable streaming ETL, analytics, and event-driven applications, while leaving out a lot of (ultimately important) details.
https://www.youtube.com/watch?v=WajYe9iA2Uk&list=PLa7VYi0yPIH2GTo3vRtX8w9tgNTTyYSux
Today’s businesses are increasingly software-defined, and their business processes are being automated. Whether it’s orders and shipments, or downloads and clicks, business events can always be streamed. Flink can be used to manipulate, process, and react to these streaming events as they occur.
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更新于 2025-01-17
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1. 深入分析和理解业务数据,优化买卖家特征,结合广告场景特性,提升买卖家匹配质量。 2. 设计广告机制,包括竞价、排序、体验优化与oCPX调价等。 3. 优化广告算法,包括但不限于召回、粗排、CTR/CVR预估和校准算法等。 4. 在大数据、算法、工程等多个技术领域提出方向性主张和架构规划。 5. 结合广告业务特点和前沿技术发展,持续进行技术应用和创新,推动AGI技术助力业务提效。
更新于 2025-06-19
社招技术
1. 负责滴滴国际化外卖广告系统的核心机制策略、模型算法的开发工作，服务不同国家和地区的用户，助力广告业务快速增长； 2. 迭代广告机器学习/深度模型，优化ctr/cvr/ltv建模、留存预估、强化学习、因果推断等算法模型，提升商业化变现效率； 3. 分析用户行为和广告流量数据，挖掘用户兴趣，理解广告内容，优化广告召回、排序和混排策略，提升流量效率； 4. 探索客户策略机制设计，包括竞价、预算分配与客户生态优化，提升广告客户增长、留存；探索冷启动、流量协同等方向；
更新于 2025-09-15
