知乎大模型安全研究员
实习兼职Data Innovation Center地点：深圳状态：招聘
工作描述
一、岗位职责（Core Responsibilities） 1 攻击面研究 ◦ 研究 LLM / 多模态模型 / Agent 在训练、微调、推理、部署全生命周期中的安全风险 ◦ 覆盖：Prompt Injection、Jailbreak、数据投毒、后门攻击、对抗样本、模型窃取/反演、成员推断、上下文污染、工具调用越权、RAG 污染等 2 安全评测与红队体系 ◦ 构建自动化安全评测 Benchmark、攻击语料库、红队测试框架 ◦ 对接 OWASP Top 10 for LLM Applications 3 防御与对齐机制 ◦ 研究并落地：输入/输出护栏、意图识别、安全对齐（SFT / RLHF / DPO / RLAIF）、拒答…
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包括英文材料
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
AI agent+
https://www.ibm.com/think/ai-agents
Your one-stop resource for gaining in-depth knowledge and hands-on applications of AI agents.
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!
RAG+
https://www.youtube.com/watch?v=sVcwVQRHIc8
Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer.
SFT+
https://cameronrwolfe.substack.com/p/understanding-and-using-supervised
Understanding how SFT works from the idea to a working implementation...
RLHF+
[英文] What is RLHF?
https://aws.amazon.com/what-is/reinforcement-learning-from-human-feedback/
Reinforcement learning from human feedback (RLHF) is a machine learning (ML) technique that uses human feedback to optimize ML models to self-learn more efficiently.
https://www.ibm.com/think/topics/rlhf
Reinforcement learning from human feedback (RLHF) is a machine learning technique in which a “reward model” is trained with direct human feedback, then used to optimize the performance of an artificial intelligence agent through reinforcement learning.
智能体+
https://learn.microsoft.com/en-us/shows/ai-agents-for-beginners/
In this 10-lesson course we take you from concept to code while covering the fundamentals of building AI agents.
https://www.ibm.com/think/ai-agents
Your one-stop resource for gaining in-depth knowledge and hands-on applications of AI agents.
React+
[英文] Quick Start - React
https://react.dev/learn
This page will give you an introduction to 80% of the React concepts that you will use on a daily basis.
https://www.youtube.com/watch?v=SqcY0GlETPk
Master React 18 with TypeScript! ⚛️ Build amazing front-end apps with this beginner-friendly tutorial.
https://www.youtube.com/watch?v=x4rFhThSX04
Learn modern React basics in the most interactive, hands-on way possible in the full course for beginners.
MCP+
https://www.youtube.com/watch?v=eur8dUO9mvE
Unlock the secrets of MCP! 🚀 Dive into the world of Model Context Protocol and learn how to seamlessly connect AI agents to databases, APIs, and more. Roy Derks breaks down its components, from hosts to servers, and showcases real-world applications. Gain the knowledge to revolutionize your AI projects!
https://www.youtube.com/watch?v=L94WBLL0KjY
Let's talk about MCP or the Model Context Protocol.
NeurIPS+
https://neurips.cc/
ICLR+
https://iclr.cc/
还有更多 •••
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