阿里巴巴阿里国际-高级推荐算法工程师(预估方向)-北京
社招全职2年以上技术类-算法地点:北京状态:招聘
任职要求
1. 计算机科学/机器学习/CV/NLP等相关专业本科及以上学历,硕士或博士优先; 2. 熟练掌握Python编程,具备扎实的机器学习理论基础,熟悉常见的推荐系统算法,熟悉TensorFlow或PyTorch等深度学习框架; 3. 具备2年以上推荐、广告、搜索等领域的算法工作经验,有用户行为序列建模、大模型微调和性能优化等经验优先; 4. 有良好的沟通协调能力,能用英语流利沟通的优先。
工作职责
1. 优化多国家、多场景、多目标的推荐预估模型提升推荐效果,包括不限于数据分析、模型开发、模型上线,针对具体问题的模型设计和开发,模型最终上线和部署; 2. 结合海量的用户行为数据、场景多场景数据和商品供给数据等进行深入分析,持续挖掘预估算法提效的机会点; 3. 针对特定问题能进行定制化的模型优化,同时负责模型部署和实验部署,拿到最终业务结果; 4. 关注推荐系统领域最新研究成果和技术动态,引入适合业务场景的前沿算法。
包括英文材料
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.
NLP+
https://www.youtube.com/watch?v=fNxaJsNG3-s&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S
Welcome to Zero to Hero for Natural Language Processing using TensorFlow!
https://www.youtube.com/watch?v=R-AG4-qZs1A&list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX
Natural Language Processing tutorial for beginners series in Python.
https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4
The foundations of the effective modern methods for deep learning applied to NLP.
学历+
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.
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/
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.
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.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
相关职位
社招3年以上核心本地商业-业
1. 负责美团团购频道推荐列表的交易效率和用户体验的优化,和团队一起完成团购频道整体的业务目标; 2. 应用深度学习、强化学习和LLM等模型和算法,对推荐列表中的供给理解、召回、排序和重排等各环节进行迭代优化,提升线上效果; 3. 参与团购业务的产品形态和业务价值讨论,深入理解业务,结合业务特点制定有效的技术方案并推进落地; 4. 与产品、算法、工程和数据团队进行紧密的合作,推进重点项目实施并取得收益。
更新于 2025-04-15
社招3年以上技术类-算法
1. 负责阿里国际站效果广告场景的算法设计和优化,包括并不限于: 2. 负责阿里国际站搜索广告Query理解、搜推广告深度召回、相关性、排序建模、机制出价、商家赋能等应用和创新 3. 负责超大规模深度学习在用户/商品表征学习、向量化召回、点击/成交转化模型预估等的应用和创新 4. 负责基于用户与商品知识图谱的稀疏特征与少样本推荐算法模型的应用和创新 5. 负责基于离线优化问题求解与在线实时调控策略的机制设计与出价 6. 负责研究&推动用户冷启动&商品冷启动在搜索&推荐的应用 7. 负责大语言模型等能力在搜索广告业务中的应用 8. 结合以上方向的探索和研究,撰写发表论文,和业界、学术界保持良好的交流
更新于 2025-10-11
社招技术类
1.迭代召回及相关性算法能力,深入理解用户意图、挖掘广告内容信息,提升广告匹配效率 2.优化点击率、转化率模型效果,利用丰富的内容和用户行为数据,并结合实际业务场景,提升模型预估准确度 3.优化广告策略算法建设,深入理解广告机制,在智能出价、拍卖机制等方向上迭代策略,提升广告主投放体验 4.跟踪学习相关领域前沿进展,探索新技术在实际业务场景中的落地
更新于 2025-03-31