AI 工程技能图谱：从 1 万条招聘数据提炼的四大核心技能
Andrew Ng 团队基于 1 万多条招聘信息、数十次专家访谈与问卷调查，发布 AI 工程技能图谱，将开发者应掌握的能力归纳为四项：构建与部署 AI 应用、软件工程基础、高效使用编码 agent、以及参与定义需求（shaping the build）。文章指出，AI 应用输出不可预测，因此需要借助统计方法与系统化的 eval 和错误分析来加以约束；软件工程知识决定了你能否用好编码 agent，而 agent 能力增强后，工程师的价值正从实现转向定义 spec 与做出成本、扩展性、安全之间的权衡。适用于想规划学习路径的开发者，以及希望以更准确标准招聘 AI 工程师的团队。
The AI Engineering Skills Map
By @AndrewYNg · 2026-08-14T16:29:42.000Z
I am delighted to present The AI Engineering Skills Map. AI allows us to build software very differently today than in 2022, and everyone with the skills to take advantage of this shift has numerous exciting project and job opportunities. But with the noisy, hype-filled, information environment around AI, what are the most valuable skills for you to learn? I have been working with my team to synthesize a map of AI engineering skills in order to help (i) developers prioritize what to learn, and (ii) employers hire skilled developers.
AI 工程技能图谱
作者 @AndrewYNg · 2026-08-14T16:29:42.000Z
我很荣幸向大家发布《AI 工程技能图谱》。如今，AI 让我们可以用与 2022 年截然不同的方式构建软件；凡是掌握相关技能、能抓住这一转变的人，都会迎来大量激动人心的项目和就业机会。但在 AI 领域嘈杂且充满炒作的信息环境中，最值得你学的技能到底是什么？我与团队一直在综合整理一份 AI 工程技能图谱，目的是帮助（i）开发者确定学习优先级，（ii）雇主招聘到熟练的开发者。
Based on an analysis of over 10,000 job postings; carrying out dozens of structured interviews with AI experts, hiring managers, and recruiters; gathering data through surveys; and synthesizing other online data, here are the four most important AI engineering skills:
- Building and deploying AI applications
- Software engineering fundamentals
- Using coding agents
- Shaping the build
You can informally think of our process as akin to running clustering on a massive dataset of jobs and expert interviews to identify the most important skills, not just today but also in the near future.
基于对超过 10,000 份职位信息的分析，对 AI 专家、招聘经理和招聘人员进行的数十次结构化访谈，通过问卷收集到的数据，再加上对其他线上数据的综合梳理，我们得出了以下四项最重要的 AI 工程技能：
- 构建和部署 AI 应用
- 软件工程基础
- 使用编码代理
- 塑造构建过程
你可以非正式地把我们的做法理解为：在一个由职位信息和专家访谈构成的大规模数据集上运行聚类，识别出最重要的技能——不仅是在当下重要，在不久的未来也同样重要。
A note on terminology: I talk about AI Engineering skills rather than the “AI Engineer” role (someone whose job is to build AI systems), because the former is much broader. All developers today should know how to work with the cloud, and only a smaller number have a “Cloud engineer” title. Similarly, all developers — full-stack engineers, data engineers, DevOps engineers, machine learning engineers, and, yes, AI engineers — will need AI engineering skills.
关于术语，先说明一点：我谈的是 AI 工程技能，而不是“AI 工程师”这一岗位（指专门构建 AI 系统的人），因为前者的覆盖面要广得多。如今所有开发者都应该知道如何使用云，只是拥有“云工程师”头衔的人相对较少。同样，所有开发者——全栈工程师、数据工程师、DevOps 工程师、机器学习工程师，以及没错，AI 工程师——都需要 AI 工程技能。
Building and deploying AI applications. The key difference between AI and non-AI applications is that the former has unpredictable outputs. When you prompt an LLM, you don’t know what you’ll get back. When you train a deep learning algorithm, you don’t know what prediction it will make on new examples. In contrast, traditional software behaves more predictably.
构建和部署 AI 应用。AI 应用与非 AI 应用的关键区别在于：前者的输出不可预测。当你向大语言模型（LLM）发出提示时，你不知道会得到什么回复；当你训练一个深度学习算法时，你也不知道它会对新样本做出什么预测。相比之下，传统软件的行为要可预测得多。
People who are skilled at building and deploying AI applications understand the building blocks of AI (such as LLMs, context engineering, RAG, agentic workflows, machine learning and deep learning) and, importantly, how to use statistical techniques to measure, steer, and govern AI systems so that they behave more predictably. A core skill in doing so is knowing how to drive disciplined evals and error analysis loops.
擅长构建和部署 AI 应用的人，理解 AI 的基本构件（如 LLM、上下文工程、RAG、智能体工作流、机器学习和深度学习），更重要的是，他们知道如何用统计技术来度量、引导和治理 AI 系统，让这些系统的行为更加可预测。做到这一点的核心技能，是知道如何驱动严谨的评估（evals）与错误分析循环。
Software engineering fundamentals. When you deeply understand how software works, you can build much more effectively. Engineering software requires making tradeoffs between cost, scalability, reliability, speed, and more. Security and privacy add further complexity.
Understanding software fundamentals allows you to recognize what tradeoffs even exist. This leads to better decisions in choosing your software stack, designing system architecture, designing your data store, testing, and so on. It also leads to much better outcomes than those for an inexperienced developer who vibe codes a solution without knowing the tradeoffs their coding agent is making — which will often be poor ones, because they don’t know what context to give their coding agent. Understanding software engineering fundamentals lets you make good tradeoffs by steering coding agents using the precise language of software engineering.
软件工程基础。当你深入理解软件的工作原理时，你构建软件会高效得多。做软件工程需要在成本、可扩展性、可靠性、速度等方面做出权衡；安全与隐私又会增加更多复杂性。
理解软件基础，能让你看到到底存在哪些权衡。这会帮助你在选择技术栈、设计系统架构、设计数据存储、测试等方面做出更好的决策。相比一个不了解编码代理正在做什么权衡、就凭着 vibe coding 写代码的新手开发者，你也能得到好得多的结果——新手之所以经常交出糟糕的方案，是因为他们不知道该给编码代理什么样的上下文。理解软件工程基础，让你能用软件工程这门精确的语言去引导编码代理，从而做出好的权衡。
Using coding agents. Using agentic coding effectively is now a key skill for every developer. When you have this skill, you have a good mental model for how agents work. You understand their limitations and how to work around them, and are able to quickly steer them — knowing how much to intervene and how much to leave them alone — to build robust software without wasting excessive time or tokens.
使用编码代理。如今，高效使用 agentic coding（代理式编码）已成为每位开发者的关键技能。掌握这项技能，意味着你对代理的工作方式有一个良好的心智模型：你了解它们的局限，知道如何绕过这些局限，并能迅速引导它们——知道该介入多少、该放手多少——从而构建健壮的软件，同时不浪费过多时间或 token。
This requires your knowing how to manage a coding agent’s context, make tradeoffs between planning and execution, and help the agent autonomously close loops by providing verifiers or evals. You also need to know how to work with a clear spec (and when not to bother doing so), orchestrate multiple agents that work together, and avoid pitfalls like risk an agent messing up your production database. Because agentic coding is evolving quickly, using coding agents skillfully means not only knowing cutting-edge practices, but also having routines to keep trying new tools and evolve your workflows as best practices change.
这要求你知道如何管理编码代理的上下文，在规划与执行之间做权衡，并通过提供 verifier（验证器）或 evals（评估）来帮助代理自主闭环。你还需要知道如何根据一份清晰的规格说明来工作（以及什么时候不必费心这样做）、如何编排多个协作的代理，以及如何避开各种坑——比如代理搞乱你的生产数据库。因为 agentic coding 演进得很快，熟练使用编码代理不仅意味着掌握最前沿的实践，还要有一套习惯：持续尝试新工具，并随着最佳实践的变化不断演进自己的工作流程。
Shaping the build. Given a clear spec, coding agents are rapidly improving at delivering to it. Thus, our work as engineers is shifting toward deciding what should be in the spec. Engineers should no longer expect to be given a pixel-perfect design and asked only to implement it. Instead, effective AI engineering requires having product sense and understanding business context and customer goals, so you can participate in shaping and driving the build.
塑造构建过程。给定一份清晰的规格，编码代理交付符合规格的能力正在快速提升。因此，我们作为工程师的工作正转向决定规格里应该放什么。工程师不应再指望拿到一份像素级完美的设计、只需要照着实现。相反，有效的 AI 工程要求具备产品直觉，理解业务背景和客户目标，这样才能参与塑造并推动构建过程。
AI also gives you the opportunity to take on greater ownership and agency than before. You can identify interesting problems and opportunities, and execute to take advantage of them in responsible ways. Taking advantage of this opportunity requires knowing how to drive projects forward. For example, knowing when to quickly build an MVP to take to users for testing, and when to slow down and take longer in order to build more carefully.
AI 还让你有机会拥有比以往更大的主导权与主动性。你可以发现有趣的问题和机会，并以负责任的方式付诸行动、抓住它们。而要利用好这样的机会，你需要知道如何推动项目前进。例如，知道什么时候应该快速做出 MVP 给用户测试，什么时候应该放慢脚步、多花时间更审慎地构建。
Underlying all these skills is a mindset of continuous learning. AI continues to change quickly, so we must all keep learning and evolving our skills to adopt emerging best practices.
支撑所有这些技能的底层心态是持续学习。AI 变化很快，所以我们都必须不断学习，不断进化技能，去采纳不断出现的最佳实践。
DeepLearning.AI’s principal focus is to help developers gain these AI engineering skills. I have more to say about each of these four skills, and will flesh out each of them in upcoming posts and share a more detailed AI Engineering Skills Map. As I look at where AI Engineering is going, I am incredibly excited about what all of us will be able to build. I hope you will play an exciting role in this future.
DeepLearning.AI 的核心使命，就是帮助开发者掌握这些 AI 工程技能。关于这四项技能，我还有很多想法要分享；我会在接下来的文章中逐一展开，并发布一张更详细的 AI 工程技能图谱。展望 AI 工程的未来，我为我们所有人将能构建的东西感到无比兴奋。希望你也能在这个未来里扮演一个激动人心的角色。