
奇虎360AI产品实习生(北京)(J11744)
实习兼职产品类地点:北京状态:招聘
任职要求
1. 教育背景:本科或研究生在读,计算机、人工智能、软件工程、信息管理等相关专业优先。 2. AI 基础与兴趣:对生成式 AI、机器学习或 NLP 有浓厚兴趣,了解大模型及 RAG 基本原理。 3. 分析与写作能力:逻辑清晰,擅长信息检索、结构化笔记与 PPT 可视化呈现。 4. 协作与沟通:积极主动,善于跨团队沟通,熟练使用协作工具。 5. 时间投入:每周至少4天,连续实习6个月及以上,支持远程。 6. 语言能力:具备良好的中英文阅读与书面表达能力。 7. 加分项:参与过 AI 相关竞赛或开源项目;具备 Python/JavaScript 基础;熟悉 Prompt Engineering 或 LLM 微调流程;对 SaaS 或企业级软件有研究或实习经历。
工作职责
1. AI 产品调研与竞品分析:跟踪国内外 LLM/RAG/Agent 等技术动态,整理市场与用户痛点。 2. 汇报材料与文档撰写:协助撰写 PRD、方案 PPT、Roadmap 等,支持内外部评审与汇报。 3. 资源协调与进度跟进:与算法、前端、后端、设计、测试团队对接,更新任务看板(Jira/飞书等),推动里程碑按期完成。 4. 数据与结果输出:产出竞品分析报告、需求洞察清单、项目周报 / Sprint 回顾等可交付成果。
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.
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
RAG+
https://www.youtube.com/watch?v=sVcwVQRHIc8
Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer.
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.
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.
JavaScript+
https://developer.mozilla.org/zh-CN/docs/Learn_web_development/Core/Scripting
[英文] Learn JavaScript
https://learnjavascript.online/
The easiest way to learn & practice modern JavaScript
[英文] Learn JavaScript
https://web.dev/learn/javascript
https://www.youtube.com/watch?v=zuKbR4Q428o
Write bulletproof JavaScript code with unit testing!
Prompt+
https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/introduction-prompt-design
A prompt is a natural language request submitted to a language model to receive a response back.
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/prompt-engineering
These techniques aren't recommended for reasoning models like gpt-5 and o-series models.
https://www.youtube.com/watch?v=LWiMwhDZ9as
Learn and master the fundamentals of Prompt Engineering and LLMs with this 5-HOUR Prompt Engineering Crash Course!
SaaS+
https://www.ibm.com/cn-zh/think/topics/saas
软件即服务 (SaaS) 是一种基于云的软件交付模式,服务提供商借此托管应用程序,并通过互联网向用户提供这些应用程序。
相关职位

实习产品类
1. 搭建 Agent,设计与优化 Prompt。 2. 参与 AI 产品研发,协助撰写 PRD 并跟进项目落地。 3. 跟踪国内外 LLM/RAG/Agent 等技术动态,调研市场与用户痛点。 4. 支持产品运营及相关工作。
更新于 2025-08-22
实习MEG
-负责百家号/度加创作工具内容生成策略的分析和制定工作,持续提升AI生成内容的质量及产量 -探索AI能力的在内容创作领域的落地方案,为创作者提供高效高质的AI创辅工具 -通过数据分析、用户调研等手段发现创作者需求,设计相应策略方案并推动落地 -与各其他团队密切配合一起工作,推动产品策略效果实现预期的效果
更新于 2025-03-18