吴恩达：AI Engineer 岗位规模远超 FDE，通才型最抢手

吴恩达老师谈「AI FDE」和「AI Engineer」 AI 在创造新岗位，但长期岗位规模上，企业内部的 AI Engineer 会远大于厂商派驻的 Forward Deployed Engine...

吴恩达老师谈「AI FDE」和「AI Engineer」 AI 在创造新岗位，但长期岗位规模上，企业内部的 AI Engineer 会远大于厂商派驻的 Forward Deployed Engineer (FDE)；眼下最有价值的是能搭应用、会用 AI 编程工具的通才型 AI 工程师。 回顾一下 AI FDE：驻场 + 深度集成 + 强交付 · 约 20 年前由 Palantir 开创：工程师进驻客户现场（如政府、隔离网环境）做深度交付 · OpenAI、Anthropic 等组建 AI FDE 团队，把工程师嵌入客户组织 · 把通用 LLM 改造成贴合业务的 定制化智能体工作流（搭建、调优、落地） · 技术 + 沟通 + 有时还需商业判断：挖需求、排优先级、讲清技术、合理 push back 和「AI Engineer」的数量关系：吴老师的判断 吴老师明确反对把 FDE 当成 AI 时代的主航道职业： 1. 企业更愿意养自己的兵 可能接受少量外部 FDE，但更希望 大量自有员工 做 AI 项目——他自己的组织也是「招 FDE，但招远更多 AI Engineer」。 2. 厂商绑定 vs 选择权（optionality） · FDE 往往深度集成 某一厂商产品，客户担心 供应商锁定 · 在「一年后哪家 AI 服务最好还说不清」的阶段，保持技术/vendor 可选性 比快速深度绑定更值钱 · 让 FDE 把流程绑死在一家厂商上，会 显著削弱未来换栈空间 结论：FDE 是重要但相对小众的交付形态；AI Engineer 才是更大、更稳的就业池。 当下真正抢手的是什么人？ 吴老师观察到需求集中在 AI Engineer，尤其是能： · 用 LLM 能力做 软件应用（prompt、智能体框架、evals 等） · 高效使用 AI Coding Agent（Claude Code、Codex、Antigravity CLI、OpenCode 等） 这是 「用 AI 组件写产品」的工程师，不一定非要驻场，也不一定代表某一家模型公司。 职业演化：会像传统 Software Engineer 一样分化 他认为 AI Engineer 会像几十年前的「软件工程师」一样 从通才裂成专才，可能包括（他也在猜测）： · AI FDE（厂商侧或咨询侧驻场型） · LLMOps Engineer · Evals Engineer · AI Data Engineer · Harness Engineer（智能体/评测 harness） · 以及 尚未命名的角色 现阶段：通才型、技能全面的 AI Engineer 仍能创造很大价值——专业化是十年量级的趋势，不是今天的入场门槛。 对「AI 砸就业」叙事的态度 他用 FDE 复兴举例：AI 在创造新工种（FDE、AI Engineer 及未来专才），因此 「工作末日 / jobocalypse」叙事过于简单。 更准确的说法是：岗位结构在变，总量与类型会重组，而不是单向消灭。 Andrew Ng @AndrewYNg One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tuning agentic workflows that suit the client’s particular needs. I’ve heard from people who are wondering anew about the FDE career path since OpenAI and Anthropic started building new teams to place FDEs within client organizations. The rise of FDEs for AI workloads is one way AI is creating new jobs (and why the jobpolcalypse narrative of upcoming job market collapse is false -- there will be many AI and non-AI jobs). However, I believe there will be far more AI Engineer jobs than FDEs, as I explain below. The FDE role was pioneered about two decades ago by Palantir, which sent engineers to government locations to work on secure, air-gapped networks. In addition to having good technical skills, FDEs need communication skills and sometimes business skills. For example, they may need to speak with clients to understand their needs, formulate a strategy to prioritize projects, explain complex technology, and respectfully push back if a client asks for something unrealistic. They’re enjoying a resurgence because of the amount of work involved in taking an off-the-shelf LLM and building it into a custom agentic workflow that fits particular business needs. However, I believe the number of AI Engineer jobs will be far larger. A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects. While my organizations do hire FDEs, we hire far more AI Engineers! Also, a common client concern is that it is hard to find vendor-neutral FDEs — they are, after all, there to deeply integrate a particular vendor’s product into a company. In this moment when it’s hard to predict which AI service will be the best one in a year’s time, optionality (the ability to pick whatever vendor turns out to fit best in the future) is very valuable. In contrast, letting FDEs tightly bind a company’s processes significantly reduces optionality. Right now, I see surging demand for AI Engineers who can build software applications using AI software components (like LLM prompting, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode). As the AI Engineer role matures, I expect it to fragment into more specialized roles, like the generic Software Engineer role from decades ago fragmented into frontend, backend, mobile, data engineering, devops, and so on. What will be the future, specialized AI engineering roles? I don’t know. Perhaps there will be AI FDEs, LLMOps Engineers, Evals Engineers, AI Data Engineers, Harness Engineers, and other roles we don’t have names for yet. But for now, I see a lot of AI engineers who are generalists create a lot of value. Skilled AI Engineers are in very high demand! As our field continues to mature over the coming decade, I look forward to new specializations within AI Engineering that create even more job opportunities. [Original text: The Batch newsletter] 🔗 View Quoted Tweet 💬 0 🔄 1 ❤️ 0 👀 378 📊 1 ⚡