{ "source_file": "0930", "source_platform": "Substack", "author": "swyx & Alessio Fanelli / Latent Space", "title_listed": "The Rise of the AI Engineer", "keywords": "AI工程师,角色定义,技能栈", "domain": "大模型算法", "content_type": "行业洞察", "is_paid_flag": "否", "url": "https://www.latent.space/p/ai-engineer", "publish_date": "2023-06", "corpus_stage": "SFT", "notes": "定义AI工程师角色，影响行业人才趋势", "id": "ad388d074085", "collected_at": "2026-10-01T19:49:49", "title": "The Rise of the AI Engineer - Latent.Space", "body_markdown": "We are observing a once in a generation “shift right” of applied AI\n, fueled by the emergent capabilities and open source/API availability of Foundation Models.\nA wide range of AI tasks that used to take\n5 years and a research team\nto accomplish in\n2013\n, now just require API docs and a spare afternoon in\n2023\n.\nImportant\n: the API line is\npermeable\n- AI Engineers can go left to finetune/host models and Research Engineers go right to build atop APIs too! This diagram has also been\ncriticized\nfor the placement of evals and data; we certainly\nagree\nevals are an important part of the job! MLR/MLEs handle foundation model concerns - aka\npretrain scale\ndata and\ngeneral benchmark\nevals; but AI Engineers should certainly view\nproduct-specific data and evals\nas their job.\n“In numbers, there's probably going to be significantly more AI Engineers than there are ML engineers / LLM engineers. One can be quite successful in this role without ever training anything.”\n-\nAndrej Karpathy\nHowever, the devil is in the details - there are no end of challenges in successfully evaluating, applying and productizing AI:\nModels:\nFrom evaluating the largest GPT-4 and Claude models, down to the smallest open source Huggingface, LLaMA, and other models\nTools\n: From the most popular chaining, retrieval and vector search tools like LangChain, LlamaIndex, and Pinecone to the emerging field of autonomous agents like\nAuto-GPT and BabyAGI\n(must-read recap from\nLilian Weng here\n)\nResearch/Progress\n: On top of this, the sheer volume of papers and models and techniques published each day is\nexponentially increasing\nwith interest and funding, so much so that keeping on top of it all is almost a full time job.\nI take this seriously and literally.\nI think it\nis\na full time job\n. I think software engineering will spawn a new subdiscipline, specializing in applications of AI and wielding the emerging stack effectively, just as “\nsite reliability engineer\n”, “\ndevops engineer\n”, “\ndata engineer\n” and “\nanalytics engineer\n” emerged.\nThe emerging (and least cringe)\n1\nversion of this role seems to be:\nAI Engineer\n.\nEvery startup I know of has some kind of\n#discuss-ai\nSlack channel. Those channels will turn from informal groups into formal teams, as\nAmplitude\n,\nReplit\nand\nNotion\nhave done. The thousands of Software Engineers working on productionizing AI APIs and OSS models, whether on company time or on nights and weekends, in corporate Slacks or indie Discords, will professionalize and converge on a title - the AI Engineer.\nThis will likely be the highest-demand engineering job of the decade.\nAI Engineers can be found everywhere from the largest companies like Microsoft and Google, to leading edge startups like Figma (via\nDiagram acquisition\n), Vercel (eg\nHassan El Mghari’s viral RoomGPT\n) and Notion (eg\nIvan Zhao and Simon Last with Notion AI\n) to independent hackers like\nSimon Willison\n,\nPieter Levels\n(of\nPhoto/InteriorAI\n) and\nRiley Goodside\n(now at Scale AI). They are\nmaking $300k/yr doing prompt engineering\nat Anthropic and\n$900k building software at OpenAI\n. They are spending free weekends\nhacking on ideas at AGI House\nand sharing tips on\n/r/LocalLLaMA\n2\n. What is common among them all is they are taking AI advancements and shaping them into real products used by millions, virtually overnight.\nNot a single PhD in sight.\nWhen it comes to shipping AI products, you want engineers, not researchers\n.\nThe AI vs ML Engineer Flippening\nI am calling attention to this trend rather than starting it. There are 10x as many\nML Engineer jobs\nas\nAI Engineer jobs\non Indeed, but the higher growth rate of “AI” leads me to predict that this ratio will invert in 5 years.\nMonthly job trends per HN Who’s Hiring\nAll job titles are flawed, but some are useful. We are both wary and weary of the endless semantic debates on the difference between AI and ML, and are well aware that regular “software engineer” roles are perfectly capable of building AI software. However a recent\nAsk HN question on How to Break into AI Engineering\nillustrates the fundamental perception that still persists in the market:\nJun 2023 screenshot:\ntop voted answers\nfor “How to break into AI Engineering”\nMost people still consider AI Engineering as a form of either Machine Learning or Data Engineering, so they recommend the same prerequisites. But I\nguarantee\nyou that\nnone\nof the highly effective AI Engineers I named above have done the equivalent work of the Andrew Ng Coursera courses, nor do they know PyTorch, nor do they know the difference between a Data Lake or Data Warehouse\n3\n.\nIn the near future, nobody will recommend\nstarting\nin AI Engineering by reading Attention is All You Need\n,\njust like you do not start driving by reading the schematics for the Ford Model T. Sure, understanding fundamentals and history is always helpful, and does help you find ideas and efficiency/capability gains that are not yet in common consciousness. But sometimes you can just\nuse\nproducts and learn their qualities through experience.\nI don’t expect this “flippening” of the curriculum to happen overnight. It is human nature to want to stuff a resume, fill out a market map, and stand out by citing deeper topics with more authority. In other words, Prompt Engineering and AI Engineering will feel inferior to people with good Data Science/ML backgrounds for a\nlong\nwhile. However, I think sheer demand-and-supply economics will prevail.\nWhy AI Engineers are Emerging Now\nFoundation Models\nare “\nfew shot learners\n”, exhibiting\nin-context learning\nand even\nzero shot transfer\ncapabilities that generalize beyond the original intent of model trainers. In other words,\nthe people creating the models don’t fully know what they are capable of\n. People who\naren’t\nLLM researchers are able to find and exploit capabilities simply by spending more time with the models, and applying them to a domain that is undervalued by research (e.g. Jasper with copywriting).\nMicrosoft, Google, Meta, and the large Foundation Model labs have cornered scarce research talent\nto essentially deliver “AI Research as a Service” APIs. You can’t hire them, but you can rent them — if you have software engineers on the other end who know how to work with them. There are ~5000 LLM researchers in the world, but ~50m software engineers. Supply constraints dictate that an “in-between” class of AI Engineers will rise to meet demand.\nGPU hoarding\n. Of course\nOpenAI/Microsoft was first\n, but Stability AI kicked off the startup GPU arms race by emphasizing their\n4\n4,000 GPU cluster.\nremember\nOctober 2022\n?\nSince then it has become commonplace for new startups like\nInflection\n($1.3b),\nMistral\n($113m),\nReka\n($58m),\nPoolside\n($26m) and\nContextual\n($20m) to raise huge seed rounds in order to own their own hardware. Dan Gross and Nat Friedman\n5\neven announced\nAndromeda\n, their $100m, 10 exaflop GPU cluster exclusively for startups they invest in. The global chip shortage\n6\nis\nreflexively\ncreating even more shortage. There will be much more capacity for AI Engineers on the other side of the API line to\nuse\nmodels, rather than train them.\nFire, ready, aim.\nInstead of requiring\ndata scientists/ML engineers\ndo a laborious data collection exercise before training a single domain specific model that is then put into production, a\nproduct manager/software engineer\ncan prompt an LLM, and build/validate a product idea, before getting specific data to finetune.\nLet’s say there are 100-1000x more of the latter than the former, and the “fire, ready, aim” workflow of prompted LLM prototypes lets you move 10-100x faster than traditional ML. So AI Engineers will be able to validate AI products say 1,000-10,000x cheaper. It’s Waterfall vs Agile, all over again. AI is Agile.\nPython + JavaScript\n. Data/AI is traditionally extremely Python centric, and the first AI Engineering tools like LangChain, LlamaIndex and Guardrails arose out of that same community. However, there are at least as many JavaScript developers as Python developers, so now tools are increasingly catering to this widely expanded audience, from\nLangChain.js\nand\nTransformers.js\nto\nVercel’s new AI SDK\n. The TAM expansion and opportunity is at least 100% bigger.\nGenerative AI vs Classifier ML\n. “Generative AI”\n7\nas a term has fallen out of favor, giving way to other analogies like “\nreasoning engine\n”, but is still useful in concisely articulating the difference between the existing group of MLOps tools and ML practitioners, and the rising, starkly different kind of persona that is best wielding LLMs and text to image generators. Where the existing generation of ML might have been focused on\nfraud risk, recommendation systems, anomaly detection, and feature stores\n, the AI Engineers are building\nwriting apps, personalized learning tools, natural language spreadsheets, and Factorio-like visual programming languages\n.\n8\nWhenever a subgroup arises that has a completely different background, speaks a different language, produces a completely different set of products, and uses a completely different set of tools, they eventually split out into their own group.\n1+2=3: The Role of Code in the evolution from Software 2.0 to Software 3.0\n6 years ago, Andrej Karpathy wrote a very influential essay describing\nSoftware 2.0\n- contrasting the “classical stack” of hand-coded programming languages that precisely model logic against the new stack of “machine learned” neural networks that approximate logic, enabling software to solve a lot more problems than could humanly be modeled. In 2023, he now notes that\nthe hottest new programming language is English\n, finally filling out the gray area in his diagram that was left unlabeled in the original essay -\nmoving from Software 2.0 to something… a LOT broader.\nUpdate:\nAndrej responds! with some disagreement\n!\nLast year, Prompt Engineering was the\nmemetic take\non how jobs would change as people began to put GPT-3 and Stable Diffusion to work. People derided AI startups as “OpenAI Wrappers”, and fretted as LLM apps proved susceptible to\nprompt injection and reverse prompt engineering\n. No moat to be found?\nBut one of the biggest themes of 2023 has very much been about\nre-establishing the role of human-written code\nto orchestrate and supplant LLM power, from\nthe >$200m behemoth Langchain\n, to\nNvidia-backed Voyager\nshowing the unmistakable importance of code generation and reuse (I recently took part in a\nChains vs Agents webinar\nwith Harrison where I expanded on the thesis of Code Core vs LLM Core applications).\nThe primary architectural divide\n: “software atop intelligence” vs “intelligent software”\nPrompt Engineering was both\noverhyped\nand\nhere to stay, but the re-emergence of Software 1.0 paradigms in Software 3.0 applications is both an area of mass opportunity/confusion, and created white space for a mess of startups:\nare you even a VC if you don’t market map?\nIt’s not going to be\njust\nhuman-written code, of course. My recent adventures with\nsmol-developer\n, the larger scoped\ngpt-engineer\n, and other code generation agents like\nCodium AI\n,\nCodegen.ai\nand\nMorph/Rift\nwill increasingly be a part of the AI Engineer toolkit. As human Engineers learn to harness AI, AIs will increasingly do Engineering as well, until a distant future when we look up one day and can no longer tell the difference.\nIt’s Time To Converge - AI Engineer Summit\nBuilders need a place to talk turpentine. This is why, after\nmonths of organizing small meetups\n, we are now announcing the first independently run, builder oriented AI conference:\nThe AI Engineer Summit\n!\ncheck out our nifty domain -\nai.engineer\n!\nIf everything in this post is resonating with you, we aim to convene all the top AI Engineers, founders, and investors together to learn about the state of the art, attend/teach workshops and find everything from the great new tool they’ll use at work, to their next new hire/cofounder/round.\nThe definitive conference\nto discuss everything that we’ve covered in the past year on this newsletter and our podcast, and more:\nAI UX\nAI Devtools\nAI Infra\nAI Agents\nNew LLM Tools, including Langchain, Vector DBs, and more\nOpen Source Models (training, finetuning, inferencing, evaling)\nI have a\nfair amount of experience\nrunning community, but have never run a 500 person conference, so I have teamed up with\nBen Dunphy\nof\nReactathon\nto put on the best AI Engineer conference in San Francisco (and online - his last conference had 20,000+ tuning in remotely).\nJoin us at\nai.engineer\n!\nWe are accepting both\nspeaker CFPs\nand\nsponsors\n(get in touch!).\nWe are the Builders\nKeen observers will have noticed that we’ve gradually\nthe Latent Space podcast and newsletter\nto cater to the AI Engineer persona. What excites me most about serving this audience is\nthe combination of techno-optimism and practicality\n.\nMarc Andreesen recently wrote\nabout how the vast majority of public AI discourse has been “hysterical fear and paranoia”, calling out that there is a whole profession of “AI safety expert”, “AI ethicist”, “AI risk researcher” paid to be doomers, but no corresponding role for builders and\nfoomers\n. On the other end of the spectrum, there are many\nunserious accelerationists\nand\nintolerable foomer threadbois\nwho spend all day on Twitter talking about a distant Utopian future, but it’s unclear what they are doing to bring it about.\nAI Engineers will tame and ride Shoggoth\n.\nLet’s make this a thing.\nThanks for the many comments and questions on\nHN\nand\nTwitter\n! We convened a snap Twitter Space to talk it through and >1,000 AI Engineers\ntuned in\n. The Rise of the AI Engineer has also been covered in\nother podcasts\n.\nAuthor’s note\n: I am especially grateful to my cohost\nAlessio Fanelli\nof\nDecibel\nand\nSarah Guo\nand\nPranav Reddy\nof\nConviction\nfor reviewing drafts of this post and providing critical feedback and invaluable support. And of course,\nBen Dunphy\nfor agreeing to cofound\nthe AI Engineer conference\nseries and network. Thank you!\n1\nAlternatives considered: “Foundation Model Engineer”, “AI API Developer”, “LLM Engineer”, but it doesn’t quite roll off the tongue, whereas “Prompt Engineer” is also too limited for the\nCode Core, LLM Shell\nwe are seeing emerge. There is also “\nMLOps Engineer\n”, but this is a\nwell established community\nhistorically focused on lower level ML concerns — a distinction we discuss later in this piece. Lastly, “\nAI Tinkerer\n” has been popularized by Alex Gravely ((\ndisputed?\n) creator of Copilot), but unfortunately doesn’t seem titleworthy.\n2\nMy full list of AI\npodcasts, newsletters\n, and\ncommunities\nis maintained on GitHub.\n3\nAnd probably could not\nexplain transformers on a whiteboard\n.\n4\nStability and AWS’ relationship on the cluster is a matter of active debate, so it is best to\nhear from Emad in his own words\non this.\n5\nI’m workshopping numeronyms for them. g12n?\n6\nMore accurately, this is an Nvidia chip shortage. This is why\nGeorge Hotz’s Tinycorp\nis so important in being able to run popular open source models on AMD cards.\nMosaic’s LLMFoundry\nalso\nreports\nAMD compatibility.\n7\nAnd, thank goodness, “Gen AI”.\n8\nOf course, when you can combine both disciplines well, you have outlier results like\nCharacter.ai\n, founded by a coauthor on the Transformers paper but finding a great use for the generative aspect of LLMs.", "metadata": { "title": "The Rise of the AI Engineer - Latent.Space", "description": "Emergent capabilities are creating an emerging job title beyond the Prompt Engineer.", "keywords": null, "author": "Latent.Space", "og:type": "article", "og:title": "The Rise of the AI Engineer", "og:description": "Emergent capabilities are creating an emerging job title beyond the Prompt Engineer.", "og:image": 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