阿里巴巴阿里国际-广告高级算法工程师(nlp/多模态/大模型)-杭州
社招全职技术类-算法地点:杭州状态:招聘
任职要求
【必备项】 1.计算机、数学或统计学相关专业硕士及以上学历 2.熟练掌握Java/C++/Python中至少一门语言,有扎实的数据结构和算法基础 3.熟悉常用的机器学习和深度学习算法及Tensorflow/Pytorch等至少一种深度学习框架 4.具备优秀的分析和解决问题的能力,良好的沟通协作能力 5.有强烈的技术热情,有皮实乐观、不畏挫折的心态;具备优秀的分析和解决问题的能力;具备优秀的学习能力和团队合作精神 【加分项】 6.有NLP/搜索/广告等机器学习相关领域实践经验者优先 7.参与过机器学习开源项目并有突出贡献者更佳 8.参加过ACM或数据挖掘&机器学习类竞赛(天池大奖赛、Kaggle)并取得好名次者更佳 9.有数据挖掘、机器学习、强化学习、信息检索、自然语言理解及计算广告学相关领域研究和实践经验,在以上领域的国际会议(SIGIR、SIGKDD、ICML、NIPS、WSDM、WWW、AAAI、CIKM、ACL、RECSYS)或者期刊上发表过论文者更佳
工作职责
我们是AliExpress广告算法团队,该岗位负责AE搜索广告的NLP&相关性、用户体验优化,包括并不限于: 1. 设计和优化搜索广告相关性下的Query理解、类目预测、深度语义相关性、商品理解、实体匹配等方向 2. 对比学习、表征学习、蒸馏学习在语义理解、类目预测、相关性判别等领域的应用和创新 3. 设计合理的全链路管控与供给策略,保证消费者体验、广告主投放效果、平台营收的良好平衡 4. LLM、MLLM在上述方向的全面应用与优化 5. 建立合理的相关性评测方法,进行数据挖掘,迭代数据标注任务,积累电商领域知识数据资产
包括英文材料
学历+
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.
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.
数据结构+
https://www.youtube.com/watch?v=8hly31xKli0
In this course you will learn about algorithms and data structures, two of the fundamental topics in computer science.
https://www.youtube.com/watch?v=B31LgI4Y4DQ
Learn about data structures in this comprehensive course. We will be implementing these data structures in C or C++.
https://www.youtube.com/watch?v=CBYHwZcbD-s
Data Structures and Algorithms full course tutorial java
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/
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.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
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.
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.
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
Kaggle+
[英文] Kaggle Learn
https://www.kaggle.com/learn
Gain the skills you need to do independent data science projects.
强化学习+
https://cloud.google.com/discover/what-is-reinforcement-learning?hl=en
Reinforcement learning (RL) is a type of machine learning where an "agent" learns optimal behavior through interaction with its environment.
https://huggingface.co/learn/deep-rl-course/unit0/introduction
This course will teach you about Deep Reinforcement Learning from beginner to expert. It’s completely free and open-source!
https://www.kaggle.com/learn/intro-to-game-ai-and-reinforcement-learning
Build your own video game bots, using classic and cutting-edge algorithms.
信息检索+
https://nlp.stanford.edu/IR-book/information-retrieval-book.html
Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.
ICML+
https://icml.cc/
WSDM+
https://www.wsdm-conference.org/
RecSys+
[英文] Recommender Systems
https://recsys.acm.org/
This site contains information about the ACM Recommender Systems community, the annual ACM RecSys conferences, and more.
相关职位
社招腾讯广告技术
1.负责广告审核多模态大模型能力建设,提升能力效率效果的天花板; 2.洞悉大模型演进趋势和技术优势,规划系统演进方向和落地,持续为业务增效; 3.与产品、运营等团队紧密合作,建设审核大模型运营能力,提升整体生产力。
更新于 2025-06-19
社招技术类
1.跟进多模态大模型的最新研究进展和技术实现(包括但不限于视频理解、视频问答、视频caption等),将多模态大模型内容理解能力与广告业务相结合,提升广告模型匹配效率 2.跟进和研发基于扩散模型的图像生成、视频生成等前沿技术,用于广告图片、视频等创意素材的内容生成 3.跟进和研发大语言模型LLM的指令微调、RLHF 等技术,结合广告业务场景落地关键词提取、智能对话、广告标题或文案生成等多个下游任务 4.结合上述多模态理解和AIGC生成能力,构建自动化素材生产和投放优化平台,挖掘站内优质内容,自动化编辑、剪辑制作素材,并基于数据驱动优化素材投前、投中环节的效果
更新于 2025-06-27
社招
1. 搜索召回算法:基于多模态&LLM大模型等能力,设计和优化1688搜索召回模块;负责文本query理解和改写;设计和优化分人群的多路召回差异化协同机制,并建立召回迭代的评价指标; 2. 搜索排序算法:设计和优化1688搜索排序下的转化率精准预估任务;深入研究全域用户行为建模、全域迁移学习任务、多模态技术方向在排序的应用;设计和优化1688搜索排序框架,围绕长期用户价值对1688排序模型的目标进行设计和优化; 3. 搜索流量机制:负责1688搜索机制策略创新和优化,包括商业化机制策略和用户增长策略的方案设计和落地,分渠道精细化优化用户的留存和活跃; 4. 搜索基础算法:在1688搜索样本和数据特征上进行精细化处理,提升模型效果的上限,通过召回/粗排/排序的模型优化和一致性提升等方式对搜索全链路进行迭代。
更新于 2025-04-16