阿里巴巴AI应用开发工程师
实习兼职淘天集团日常实习生批次地点:杭州状态:招聘
任职要求
【必备项】 * 本科及以上学历,计算机、人工智能相关专业; * 扎实的编程基础,熟练掌握Java/Python/TypeScript等一门或多门编程语言,了解深度学习框架; * 熟悉大语言模型(LLM)的基本原理和应用场景; * 了解模型训练、微调的基本流程和最佳实践; * 具备良好的系统设计能力和工程实践经验; * 出色的问题分析和解决能力,能够独立处理技术难题; * 良好的团队协作能力和沟通技巧。 【加分项】 * LLM经验:有大语言模型训练、微调或部署经验,熟悉LLM 的后训练、RLHF、PEFT等技术; * 开发框架:熟悉LangChain、LangGraph等LLM应用开发框架,熟悉深度学习框架(PyTorch/TensorFlow),有Transformer等模型实战经验优先; * 业务经验:AI应用开发经验,了解AI产品从概念到上线的全流程; * 技术广度:熟悉云服务、容器化技术、分布式系统等相关技术; * 创新能力:能够提出创新的技术方案和应用场景,有成功案例最佳; * 算法基础:具备机器学习、深度学习的扎实理论基础; * 工程实践:有大规模AI系统开发和运维经验以及相关实践; * 知识储备:了解向量数据库、RAG、知识图谱等相关技术,有实践经验优先。
工作职责
1. 负责企业级智能办公助手AI Agent的架构设计与核心功能开发,打造智能化、自动化的办公体验; 2. 参与大模型微调、强化学习(RLHF)与Agent行为调优,优化Agent任务执行效率与响应质量,提升任务执行准确率与用户体验; 3. 负责Agent系统关键技术攻关,包括但不限于:多轮对话管理与上下文理解、复杂任务分解与规划、多模态交互与工具调用、智能体协同决策机制; 4. 与业务团队紧密协作,深入分析场景需求,设计端到端解决方案,实现Agent能力与业务流程的深度融合; 5. 建立Agent系统的性能和效果的评估体系,包括准确率、实时性、可靠性等关键指标的量化监控; 6. 持续跟踪AI前沿技术(如ReAct、CoT、MCP、A2A),推动新技术在工程场景落地与性能优化。
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.
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.
TypeScript+
https://www.youtube.com/watch?v=JHEB7RhJG1Y
Master TypeScript from basics to advanced concepts through hands-on tutorials covering type annotations, generics, data fetching, Zod library, and more, with practical challenges for effective real-world application.
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
系统设计+
https://roadmap.sh/system-design
Everything you need to know about designing large scale systems.
https://www.youtube.com/watch?v=F2FmTdLtb_4
This complete system design tutorial covers scalability, reliability, data handling, and high-level architecture with clear explanations, real-world examples, and practical strategies.
开发框架+
[英文] Understanding Modern Development Frameworks: A Guide for Developers and Technical Decision-makers
https://www.freecodecamp.org/news/understanding-modern-development-frameworks-guide-for-devs/
LangChain+
https://python.langchain.com/docs/tutorials/
New to LangChain or LLM app development in general? Read this material to quickly get up and running building your first applications.
https://www.freecodecamp.org/news/beginners-guide-to-langchain/
LangChain is a popular framework for creating LLM-powered apps.
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.
Transformer+
https://huggingface.co/learn/llm-course/en/chapter1/4
Breaking down how Large Language Models work, visualizing how data flows through.
https://poloclub.github.io/transformer-explainer/
An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.
https://www.youtube.com/watch?v=wjZofJX0v4M
Breaking down how Large Language Models work, visualizing how data flows through.
分布式系统+
https://www.distributedsystemscourse.com/
The home page of a free online class in distributed systems.
https://www.youtube.com/watch?v=7VbL89mKK3M&list=PLOE1GTZ5ouRPbpTnrZ3Wqjamfwn_Q5Y9A
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.
RAG+
https://www.youtube.com/watch?v=sVcwVQRHIc8
Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer.
相关职位
社招GN06
1. 负责生成式 AI 应用的后端架构设计与核心模块开发 2. 推动 AI 工程化落地,实现算法到工程的完整闭环 3. 与产品团队紧密协作,探索 AI 的创新场景,快速验证技术原型并落地 4. 跟踪前沿AI技术发展,提供创新性技术解决方案
更新于 2025-05-07
实习淘天集团2026
1. 探索面向企业员工办公场景智能体架构,参与企业级AI Agent系统的设计与开发; 2. 整合RAG、知识图谱等关键技术,开发高性能推理框架; 3. 开展大模型微调、强化学习(RLHF)与Agent行为调优,解决复杂任务中的实时性、稳定性与资源消耗等问题,提升任务执行准确率与用户体验; 4. 协同业务、产品团队,主导从需求分析到技术方案设计落地等关键环节,构建端到端的技术实现路径,实现Agent能力与业务场景的深度融合; 5. 参与优化Agent自主决策、任务规划、多模态交互等能力,构建Agent系统的量化评估体系; 6. 持续关注AI领域最新进展,研究Agent领域的前沿技术(如ReAct、COT、MCP等多智能体协作),并推动工程场景落地与性能优化; 7. 重点围绕自营类业务的供应链、消费、风控等场景进行AI创新应用,进一步降低用户消费决策成本,提供突破性消费体验。
更新于 2025-05-07
校招研发类
1、负责AI算法的工程化落地或AI技术在智能化产品的场景落地,能够在指导下完成算法引擎的能力构建或智能助理类产品的业务应用开发; 2、跟踪行业动态和新技术发展,持续通过新技术的引入及落地支持AI产品体验提升。
更新于 2025-05-28