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The Rise of the AI Engineer

219 points by swyx 3y ago

swyx 3y ago
hi HN! am writing this up as a recap of the enhanced role of code in LLM applications, and the emerging professionalization that will happen as a result. would welcome any and all feedback! I'm also soft launching the conference I am planning for Oct. Join us if you are in SF (will be streamed) https://ai.engineer/ https://ai.engineer/
forrestbrazeal 3y ago
Hey Shawn! Always enjoy your writing. I think you've done well laying out what a lot of people want "AI Engineer" to mean at this moment in time. My concern (coming fresh out of the absolute semantic nightmare that was the "serverless" community) is that the term AI is so hopelessly overloaded, and has been such a moving target over the years, that it's unlikely that a plurality of people will ever share your mental model of what an "AI Engineer" is/ does / knows / is paid. Personally I've been using Generative Engineering / GenEng [0] to describe the professional practice of building stuff with AI as your pair programmer. I recognize some are pulling away from the term "generative", but to me it feels like a better anchor into the specific flavor of AI we're talking about. [0] https://cloud.google.com/blog/products/ai-machine-learning/the-rise-of-geneng-how-ai-changes-the-developer-role https://cloud.google.com/blog/products/ai-machine-learning/t...
- giovannibonetti 3y ago
  Maybe Text Processing Engineer or Text Engineer for short could express well that this person handles both text analysis and synthesis. It is analogous to the term Data Engineer, which describes someone that handles data processing and pipelines in general.
- ehnto 3y ago
  Generative isn't perfect for similar reasons perhaps, I consider procedural generarion adjacent to machine learning based generation so it would need to encompass both to make sense to me.
- swyx 3y ago
  hey Forrest! thanks so much! agree with the semantic overload risk, but i think at this point Worse is Better is applying here. like I said in the piece I'm not starting the trend, just calling out that it's already under way.
- sanderjd 3y ago
  Hmm, I think "the professional practice of building stuff with AI as your pair programmer" is just "software engineering". That is, it's just one more tool to use to do the existing work. We never had "Search Engineering" or "StackOverflow Engineering" to describe the practice of building stuff using web search and stack overflow as tools...
  - __loam 3y ago
    Imagine calling yourself a compiler engineer because you use a compiler.
Xen9 3y ago
I predict Cognitive Engineer / Cognitive Architect will become a thing.
- __loam 3y ago
  And it will have nothing to do with actual cognitive science, just like neural networks, artificial intelligence, and machine learning.
29athrowaway 3y ago
When you see the taxonomy of AI and ML you will learn that saying "AI engineer" is vague and essentially useless. Do you use depth first search at work? Congratulations, you are an AI engineer. Do you use any form of search or information retrieval at work? Congratulations again. Do you have a system that makes decisions based on a decision tree? Again, congratulations. In fact, stop using "AI" at all. Your washing machine is an AI agent, it uses fuzzy logic. Are you using LLMs? then just say you work with LLMs. Search engineers don't call themselves AI engineers.
vsareto 3y ago
For this conference, I think it would also be great to have a presentation about job requirements at various levels, then continue to cite that presentation often. It's important to have an on-ramp for people interested in this space with well-known requirements to aim for. This also makes it easy for companies to know what they're getting with this title. The ambiguity of full stack engineers meant that it was a dumping ground for responsibilities.
- simonw 3y ago
  It feels far too early to me for there to be any clarity on job requirements. If I was hiring for an "AI engineer", I'd want to see examples of things they had built.
- swyx 3y ago
  (author here) one of my ideas of research for this blogpost was to actually ask a few employer friends who want to hire AI Engineers for a job description, and then put out a generic JD that people can use and clone (kind of a SAFE for AI Engineers if you think about it). however I feel like it is too early still to prescribe something. I'll maybe put up a strawman for the conference and would love feedback then.
cbm-vic-20 3y ago
I'm a natural skeptic, and I believe we're still on the rising edge of the "AI" hype cycle. Five years ago, it was "blockchain", and everyone was trying to ram blockchain into everything, attracting lots of VC and media attention, etc. It seems that blockchain is beyond the honeymoon phase: I haven't seen an NFT or even a Bitcoin headline in HN for a while. So I'm trying to wrap my head around what an "AI Engineer" is. As I see it, it's all about calling a function that takes some text as an input, and getting some text as output. That function, of course, is being run on some big hardware that in most cases, you don't own. So is the "engineering" part of this finessing the input and massaging the output? Do most "AI Engineers" actually understand what's going on in that function, beyond what they learned in the "LLM 101" videos and articles that have been flooding the web over the past year? While an application developer doesn't need to know all of the ins-and-outs of the underlying operating systems they run on, those developers that do have a deep understanding of the OS are the ones who write more performant code, and can really get to the bottom of issues that arise do to how their code uses the OS. Can the same be said of "AI Engineers"? All of a sudden, everyone's an AI Engineer. Where where these experts hiding five years ago? https://trends.google.com/trends/explore?date=today%205-y&geo=US&q=blockchain,GPT https://trends.google.com/trends/explore?date=today%205-y&ge...
- voz_ 3y ago
  Imagine comparing blockchain and AI.
  - aerhardt 3y ago
    LLMs and GenAI are already useful at scale but the current hype that they will lead to infinitely generalizable models, myriad groundbreaking applications in all fields and industries, or even AGI could be overblown - let's at least admit that as a possibility.
    - lost_tourist 3y ago
      If AGI reached "human level" capabilities and took off from there how in the world could that possibly be overblown? That would change everything, comparing it to discovering fire would probably be apt, and maybe the first time in recorded history that making that statement isn't overly dramatic.
Der_Einzige 3y ago
If you are someone who was working in LLMs/Generative AI before it got "cool", The market has fundamentally changed and changed extremely in your favor. Talent is extremely scarce and expensive right now in this space. Ask for well above FAANG compensation. Try to shop around for offers. I had 4 offers and a bidding war for my talent (with no leetcode - but I have an extensive github and publications about LLMs), when previously these same companies would have brutally leetcoded me. I thought that there would be a ton of people getting really good really fast with Generative AI. It turns out that most people are terrible at using the tools, and there's even research about this right now - https://dl.acm.org/doi/10.1145/3544548.3581388 https://dl.acm.org/doi/10.1145/3544548.3581388
- sanderjd 3y ago
  I'm someone who would like to get "really good really fast", but have found that the on-ramps remain pretty weak. For instance, I have yet to find a good book on the subject. There are a ton of tutorials and articles, but it's maddening to try to cobble together any depth of understanding from those little nuggets. And there are tons of good papers on how the systems actually work, but these are not very useful for people who are new to this set of tools to figure out how to use them to create useful things. But I think this will hole will close up incredibly quickly over the next six months. (And it's yet another really good opportunity for people like you to be involved in that gold rush!) Edit to add: For instance, the article lightly lambasts peoples' recommendations to read up on AI / ML fundamentals, and contains this: > n the near future, nobody will recommend starting in AI Engineering by reading Attention is All You Need, just like you do not start driving by reading the schematics for the Ford Model T. But, amazingly, it doesn't actually suggest an alternative starting point*. There is this call to action about the conference, but presumably that will mostly be people who have already figured this out to some degree. But (to carry on the author's analogy) what is the recommendation for driver's ed?
  - swyx 3y ago
    we haven't launched it widely yet but if you peek at the top level nav you'll find the course we are working on :) https://www.latent.space/s/university https://www.latent.space/s/university
supportengineer 3y ago
There are a couple types of roles that sounds really interesting to me. One would be taking some proprietary data and training LLM in a format it could use. One company I know has a database of cars. They want to train their LLM with some inventory facts like "We have a Ford Mustang on the lot whose vin is ABC123 and it has the following features...". And then another role would be writing the prompts for API calls to the LLM. "Write a report on all cars currently in the lot which are available for sale and have the following features...."
- wredue 3y ago
  This doesn’t seem like a thing any business should need AI for. Filtering a known list for a report takes a few seconds at most, and is much more reliable than LLMs.
- Jtsummers 3y ago
  > They want to train their LLM with some inventory facts like... So are they actually intending to retrain their LLM every time the inventory changes? Because, otherwise, how is it going to "know" the current state of the inventory? This is useless after a single sale or a single new delivery without retraining. (And it's likely useless before that anyways.) And if they already have a database of inventory data with all this then they could just generate a report the "old fashioned" way that's worked for decades.
  - wizofaus 3y ago
    I would expect the solution is to take the NL question and get GPT to transform it into a SQL (or similar) statement to extract the data. Then another call (or set of calls) to generate "reports" summarizing the data returned by the DB query.
    - deleted 3y ago
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    - john2x 3y ago
      At which point, maybe a "GUI-interface" will be cheaper to build and maintain in the long run. Now if AI could automagically update inventory data with what's actually physically happening on the lot, that would be cool.
cj 3y ago
I can see a world where "ML Engineer" (or similar) is someone that's hired to solve a known problem (whether it be with classifiers, LLMs, neural nets, etc), whereas a "AI Engineer" (or whatever the title) is hired to figure out how the hell to capitalize on the AI hype, without a specific problem to solve. IMO right now we're entering the "Peak of Inflated Expectations" in Gartner's hype cycle model. https://en.wikipedia.org/wiki/Gartner_hype_cycle#/media/File:Hype-Cycle-General.png https://en.wikipedia.org/wiki/Gartner_hype_cycle#/media/File... Lots of companies want to jump on the AI bandwagon, but they don't really know what to do or how to leverage it. What the LLM community needs now is for companies to leverage and productize these LLM models for truly game changing use cases. If that doesn't happen soon, the hype will start to fade and lose momentum ("trough of disillusionment"). I'd really love to see some killer use cases emerge soon.
- yacine_ 3y ago
  The right way to leverage AI is to use it to do ye olde engineering, faster. Actually, most AI use cases are in fact that! Ye olde engineering - data pipelines, automation. Stuff we've been doing since before I was born. Everything else - transformative tech, will hit the open domain fairly quickly, is my bet. Similar to databases.
- richardw 3y ago
  I’ve been thinking the leading edge is already in the trough of disillusionment! At least on HN etc. We see lots of reports of limitations on HN. Those in the know don’t trust it nearly as much as the public and all the CEO’s of businesses drooling to install AI and have the money go up. Going to have to dig our way through those issues to get real value.
  - moffkalast 3y ago
    Well you have to consider the inherent bias on HN, in that the trough of disillusionment is HN's default state of mind regarding literally every topic. It's really rare to see a thread where most of the comments aren't negative.
avereveard 3y ago
The rise of the monkey king of infinite monkeys I'm using LLM for many things, but let's not pretend it's engineering
preommr 3y ago
Oh God, this is going to be like DevOps all over again isn't it? Where there's people constantly gluing together things from different companies. I foresee layers and layers of abstraction dependent not on technology spec but on some company's poorly maintained docs and apis that you get by calling some service rep.
- sanderjd 3y ago
  I have found data engineering to feel that way as well. Just throwing darts at a board of vendor solutions, trying to figure out how to cobble them together into something useful. I am worried this is going to be that as well. I'd like to learn more about how to self-host systems that are large enough to be useful, rather than doing this cobbling together proprietary APIs thing.
paxys 3y ago
I'm not sure I agree with their reasoning. There are a lot of generalist backend/infra engineers who are working in the AI space. What is the special skillset that distinguishes them from all others? If you say that they are "AI engineers" because they are working on AI as a product, then should we also have "advertising engineers", "billing engineers", "API engineers"? The reality is that tons of people just build a trivial app calling an OpenAI API and put "AI engineer" on their resume to capitalize on the hype.
- zwaps 3y ago
  Tech companies ARE doing that. I have met "Customer Success Engineers" and other managerial or marketing related roles with the moniker Engineer. People want to call themselves engineers.
rufius 3y ago
AI/ML Engineers will be our modern day variant of the Mystic. They'll whisper sweet nothings into the ears of their models, looking at the goat entrails of what their models output and tell us of their predictions. It's going to be a shit show.
- amelius 3y ago
  This stuff is going to be so vague that an AI will be able to do it.
- somewhat_drunk 3y ago
  Hilarious!
Zetobal 3y ago
It's not the AI Engineer it's the AI Operator that everyone is looking for.
- sanderjd 3y ago
  "Operator" feels right to me as well here.
  - john2x 3y ago
    Nice. One step closer to having Smooth Operator be a real title
janalsncm 3y ago
I still feel a bit strange about calling someone an “AI engineer” and I think there are a few reasons. 1. AI is poorly defined. This is a fundamental problem behind almost every conversation on the “topic”. Depending on the context, AI can mean anything from a decision tree to deep neural networks to science fiction. 2. Engineering implies a deeper level of understanding. If you want to engineer a system, you need a deeper understanding of how each of the components work. Using a tool does not make one a tool engineer. It makes them a 21st century blacksmith. Does calling an LLM API make me an AI engineer? If so, calling a weather API makes me a weatherman. 3. This is a relatively new area, and make no mistake, it is a new area. 2 years ago most of the tooling around ML work meant you had to get your hands dirty. To use BERT, you pretty much needed to learn about tokenization and attention masks and CUDA. Not so much with GPT3. So it seems premature to even circumscribe at the moment. What I am not saying: I don’t think one needs to take a class in linear algebra to work with this stuff. I also never believed in calculus for CS students, which may be a minority opinion too.
- deleted 3y ago
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minimaxir 3y ago
I would never be able to be hired as an "AI Engineer" or "Prompt Engineer" despite my extensive AI portfolio because my job title is Data Scientist and the discrepancy would confuse most hiring managers. Job titles are moving faster than career ladders.
Animats 3y ago
Has "robopsychologist" shown up in job ads yet?
m3kw9 3y ago
AI engineer is a hype term for someone that can incorporate LLM to solve an engineering problem. It isn’t that hard to use OpenAI apis, LLM are like super abstractions. You tell it in natural language, as opposed to a specific programming language. It’s still a very narrow field. The hype is real but it’s getting a bit frothy
__rito__ 3y ago
> "the fundamental gatekeeping that still persists in the market" This person just called me a gatekeeper when just our definitions of what an AI Engineer is different... all while discussing the ambiguities of the term "AI Engineer". Well, that's the internet for you! My intentions were never of gatekeeping, our definitions of the term are wildly different. I laid the beginning part of a roadmap for a person who wants to solve problems that exist in the real world with Deep Learning- by choosing how to structure data, training models with existing architectures (and optimizers, loss functions, activation functions, and so on) and creating new ones from scratch- and to deploy them to solve the problem in the physical space. Although, I have not spent hours on what the term "AI Engineer" should mean, it is not prompt engineering to me. If there is a consensus in future on the term meaning prompt engineering, then I will not give any advice on that, because I don't know much. Neither will I fight the definition. My definition, still is the person who develops new kinds of microwaves as opposed to the author's whose definition is that of a cook who uses the microwave, by creating new recipes and dishes. That's fine, but didn't liked being called a gatekeeper.
- swyx 3y ago
  apologies for that - it came from a friend who was reviewing that HN thread and used that word - but it was my choice to repeat that word in my writeup. I didnt take your feelings into account when using what I knew to be a loaded word, and in retrospect it did nothing to further the strength of my argument at all. I'm sorry. I've removed it.
  - __rito__ 3y ago
    Thank you so much. As I mentioned, my definition was just different. And I didn't even mean to gatekeep. My spirit was: "if I can do it, many more people definitely can, too". So I went on and shared an outline of my journey. That's all. No hard feelings.
charles_f 3y ago
There's a bit of snake oil in all of that. By any means ML is a very specialized subfield, where you need solid math basis and a deep understanding of the science behind it all. But I struggle to see the same thing for AI. If by "AI engineers" you mean someone who builds an LLM*, then it's very much just ML. If you mean someone who integrates with the LLM someone else built, then it's very much just backend work. Sure, you might need a few days to understand a few concepts, I've integrated with Paypal in the past and that doesn't make me a payment for engineer. * wont even get into the argument of labelling LLMs with AI
- fragmede 3y ago
  Well, and frontend work. ChatGPT wouldn't been anywhere near the product it is without the webterface. As far as PayPal integration, no but it makes you the subject matter expert (SME) in the room above a room full of people who aren't, and maybe aren't even developers.
- senko 3y ago
  If you have React engineers, why couldn't you have AI engineers? I think the post is right to try to delineate the roles of "builds LLMs or other ML models" and "uses those models", as (as you noted) the skills required are very different (vs. "creates a JS framework" vs "uses a JS framework" are the same skills, just on another level). "AI engineer" is, as you say, inaccurate, but as swyx says, it's least cringy of the alternatives. > Sure, you might need a few days to understand a few concepts, I've integrated with Paypal in the past and that doesn't make me a payment for engineer. No, but the post explicitly calls out the breadth of knowledge that the future AI engineers will have to have. It's not something you can pick up in a few days, even now, if you want to be on top of things. I can look up some docs and hook up static file hosting on S3 and that doesn't make an AWS engineer. But there are people who full time work on providing solutions using AWS-everything and have built their entire careers (and companies) on top of that specialization. The difference in scale is the difference in job description.
  - ignoramous 3y ago
    > If you have React engineers, why couldn't you have AI engineers? We most definitely could. That's one way to prop up a cottage industry, stay relevant, command hefty salaries, and maintain that charade of importance through complexity by creating Frankenstein features in search of problems and promotions.
vjust 3y ago
recently saw a demo app built on top of GPT3. Used a rest API for prompts. The backend was hooked to a corpus of PDFs and a SQL Database with financial data. What changed was the queries were English/natural language. The query language has changed. That's the R in CRUD. I wonder if the C, U, and D will also change. While this is one of many types of AI, it means commands will be in natural English. This might be a big deal for UI builders because how we ask questions has changed to NL.
- wizofaus 3y ago
  I'm curious about this sort of use case - how long does it take for a GPT-based system to process a bunch of documents it's never seen before so that you can perform NL searches on them? Assuming something ike a million total pages of text are we talking minutes? Hours? Days? And is it at all feasible to ensure whatever factual information is returned is only sourced from said documents, vs being "hallucinated" by virtue of whatever weights exist based on the core training corpus?
amelius 3y ago
AI engineer is the new webdeveloper.
intellectronica 3y ago
In my experience people who make dismissive comments about AI (in its current form) and AI Engineering as a discipline tend to have very little experience with and superficial understanding of AI. Once you start using it seriously as part of your engineering stack it quickly becomes clear that there’s a lot of detail and complexity that more than justify specialisation.
- z3c0 3y ago
  Heavily disagree, and this just seems like posturing from the other side of things. I understand AI rather heavily, and have been a part of the scene far before the explosion of even the first GPT model. Frankly, I've been quite irked by all the people who claim themselves "ML engineer" when most of what they do is glue huggingface models together. They're doing none of the work required to make models, which is quite extensive. They understand very little about the models they wield. This new wave, I find even more irksome. "Prompt engineer" and "AI engineer" and aren't much more than pseudoscientists who fanangle API inputs until they get what they want. This is in stark contrast to all the maths I perform to understand a model's performance.
  - intellectronica 3y ago
    I think you've just proven my point.
    - z3c0 3y ago
      On reddit, this might seem like a burn, but here, you need to explain that claim and not just hope somebody rides with it. How is tinkering with an API an actual understanding of AI? Please actually explain
  - foobiekr 3y ago
    Strong agreement. What we have right now is a lot of people doing light glue code and pretending it is engineering.
    - 3y ago
boredumb 3y ago
I think it's all just being more accessible for people like me to integrate into applications via AIs. With that said - the barrier for entry also lowered substantially in the last few years to a point where implementing your own neural network from scratch with Pytorch is something you can do in a few (took me a weekend) hours coming in blind, but reading the docs and scrolling some existing repositories. I remember a decade ago taking a stab at it and being very much over my head. Not sure if i'm getting older and there's less magic going on around it or if it's the ecosystem/docs/examples/pytorch/jax/hours of youtube content/etc but somewhere in there, at least for me personally, it's gotten much more accessible.
- amilios 3y ago
  Personally I think it's a combination of a) much better resources being available than in the past, which explain concepts clearly and in relatively simple terms, and b) much better tooling existing now than ever before, so many things that you used to need to do "by hand" are now taken care of by relatively standardized tooling. Even just automatic differentiation engines are a huge deal, not having to backprop "by hand" (first release of TensorFlow was 7 years ago, so if you looked at it a decade ago I'm assuming you had to implement backprop yourself). Beyond that another jump was from Tensorflow's delayed execution/compilation model (it really was a headache to work around its APIs/set up the computation graph) as well as just having a generally ugly API, to PyTorch's "literal" setup, where it feels like you're just writing regular Python code and performing operations "normally"
ilaksh 3y ago
I mean, I consider myself an effective "AI Integration Engineer" but I _have_ done an Andrew Ng ML Coursera course and also built a MLP in C++ from scratch. But none of that really matters when it comes to applying something like GPT or Stable Diffusion to a particular application. You just send appropriate text to a model via an API call. I think "AI Integration Engineer" is a bit more of an accurate title because usually it's about integrating AI into existing products or domains. And "AI Engineer" by itself sounds a little bit like you might be claiming to be a PhD. But just a bit. I think it's fair enough to shorten it to AI Engineer though so we don't all have to type out "integration" over and over.
spaintech 3y ago
My thought on what an AI engineer is; an individual who uses AI and machine learning techniques to develop applications and systems that can help organizations increase efficiency, reduce costs, increase profits, and make better business decisions 1 . AI engineers play a crucial role in helping enterprises leverage the capabilities of large language models (LLMs) like GPT-3 and beyond, this means that they will, “Develop Domain-Specific Models” AI engineers can fine-tune LLMs to create domain-specific models to ensure that the data and the business process align to provide more context. For example, a model fine-tuned on medical literature can assist doctors in diagnosing diseases or answering patient queries, ect. “Data Preparation and Management” By this, I presume they will be involved in cleaning the data, dealing with missing or inconsistent data, and ensuring the data is representative of the task the model will be performing. “Integration with Existing Systems” they will help integrate these AI models into the existing IT infrastructure of an enterprise. This can involve developing APIs, designing user interfaces, and ensuring the model's outputs can be used by other systems or processes. Etc… I believe that they enable organizations transform data into knowledge and ultimately, into wisdom - the highest level of data maturity. This is particularly true when dealing with domain-specific models, which can provide highly targeted and context-specific insights. When you can reach a level of data maturity that enbles actions on data, this is where AI will drive the change that is just starting. It’s very exciting to watch it unravel, not because of the possibility of generating a sentient machine, but with the possibility that will drive new fields of discovery that has been under our noses, and these tools will help us make sence of it all, IMHO.
Uptrenda 3y ago
Tl; dr: a bunch of know-nothing plumbers swerve their digital cars from last years buzzword (blockchain) towards AI, narrowly missing a collision with a crowd of VCs. Upon arriving at the AI convention they switch hats to AI where they list themselves as experts (they can use the Chat-GPT API.) How any of this works or what problems may arise are a job for the cleanup crew (paid less, they aren't calling themselves experts.)
- Sai_ 3y ago
  This is unnecessarily combative though it’s also probable I’m reading too much into this since I’m one of those swervers - from cloud computing guru to AI aficionado. Personally, my mental bent is towards tinkering - I like wiring things up to make them work. I used to do that with wires and screwdrivers as a kid, now I read api docs and do the same thing. The point being, it’s likely that I’ll continue connecting things to each other just to see how they work and offer that as a service until the day I die. You need people who invent the algos, people to wire up algos to each other, people to sell this service to people, people to want to pay for that service, and so on. None is better than the other. They’re all necessary for people to put food on the table and live the life they want. Sneering at them is pointless.
mercurialsolo 3y ago
With the rise of LLM's there is definitely a new skillset needed in the bag of engineers - one who know how to align/instruct LLM's towards an objective. This new role is essentially a mix of standard programming logic + ability to instruct LLM's using language (natural language programming). That's been the primary difference I have seen in this emergent field of designing agents. Most agents need some form of formal instructions tied with designing prompts - which are a mix between formal programming and natural language. This needs a slightly different skill set from the traditional backend engineer or services engineer. This is not a ML or data role either. Rather than just a prompt engineer, the case for which could be made like a specialist designer - this role maybe more akin to a full-stack developer role who knows how to align LLM's or Diffusion models towards a multi-step goal.
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lee101 3y ago
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chwa982 3y ago
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219 points by swyx 3y ago

swyx 3y ago

hi HN! am writing this up as a recap of the enhanced role of code in LLM applications, and the emerging professionalization that will happen as a result. would welcome any and all feedback! I'm also soft launching the conference I am planning for Oct. Join us if you are in SF (will be streamed) https://ai.engineer/ https://ai.engineer/

forrestbrazeal 3y ago

Hey Shawn! Always enjoy your writing. I think you've done well laying out what a lot of people want "AI Engineer" to mean at this moment in time. My concern (coming fresh out of the absolute semantic nightmare that was the "serverless" community) is that the term AI is so hopelessly overloaded, and has been such a moving target over the years, that it's unlikely that a plurality of people will ever share your mental model of what an "AI Engineer" is/ does / knows / is paid. Personally I've been using Generative Engineering / GenEng [0] to describe the professional practice of building stuff with AI as your pair programmer. I recognize some are pulling away from the term "generative", but to me it feels like a better anchor into the specific flavor of AI we're talking about. [0] https://cloud.google.com/blog/products/ai-machine-learning/the-rise-of-geneng-how-ai-changes-the-developer-role https://cloud.google.com/blog/products/ai-machine-learning/t...

giovannibonetti 3y ago
Maybe Text Processing Engineer or Text Engineer for short could express well that this person handles both text analysis and synthesis. It is analogous to the term Data Engineer, which describes someone that handles data processing and pipelines in general.
ehnto 3y ago
Generative isn't perfect for similar reasons perhaps, I consider procedural generarion adjacent to machine learning based generation so it would need to encompass both to make sense to me.
swyx 3y ago
hey Forrest! thanks so much! agree with the semantic overload risk, but i think at this point Worse is Better is applying here. like I said in the piece I'm not starting the trend, just calling out that it's already under way.
sanderjd 3y ago
Hmm, I think "the professional practice of building stuff with AI as your pair programmer" is just "software engineering". That is, it's just one more tool to use to do the existing work. We never had "Search Engineering" or "StackOverflow Engineering" to describe the practice of building stuff using web search and stack overflow as tools...
- __loam 3y ago
  Imagine calling yourself a compiler engineer because you use a compiler.

Xen9 3y ago

I predict Cognitive Engineer / Cognitive Architect will become a thing.

__loam 3y ago
And it will have nothing to do with actual cognitive science, just like neural networks, artificial intelligence, and machine learning.

29athrowaway 3y ago

When you see the taxonomy of AI and ML you will learn that saying "AI engineer" is vague and essentially useless. Do you use depth first search at work? Congratulations, you are an AI engineer. Do you use any form of search or information retrieval at work? Congratulations again. Do you have a system that makes decisions based on a decision tree? Again, congratulations. In fact, stop using "AI" at all. Your washing machine is an AI agent, it uses fuzzy logic. Are you using LLMs? then just say you work with LLMs. Search engineers don't call themselves AI engineers.

vsareto 3y ago

For this conference, I think it would also be great to have a presentation about job requirements at various levels, then continue to cite that presentation often. It's important to have an on-ramp for people interested in this space with well-known requirements to aim for. This also makes it easy for companies to know what they're getting with this title. The ambiguity of full stack engineers meant that it was a dumping ground for responsibilities.

simonw 3y ago
It feels far too early to me for there to be any clarity on job requirements. If I was hiring for an "AI engineer", I'd want to see examples of things they had built.
swyx 3y ago
(author here) one of my ideas of research for this blogpost was to actually ask a few employer friends who want to hire AI Engineers for a job description, and then put out a generic JD that people can use and clone (kind of a SAFE for AI Engineers if you think about it). however I feel like it is too early still to prescribe something. I'll maybe put up a strawman for the conference and would love feedback then.

cbm-vic-20 3y ago

I'm a natural skeptic, and I believe we're still on the rising edge of the "AI" hype cycle. Five years ago, it was "blockchain", and everyone was trying to ram blockchain into everything, attracting lots of VC and media attention, etc. It seems that blockchain is beyond the honeymoon phase: I haven't seen an NFT or even a Bitcoin headline in HN for a while. So I'm trying to wrap my head around what an "AI Engineer" is. As I see it, it's all about calling a function that takes some text as an input, and getting some text as output. That function, of course, is being run on some big hardware that in most cases, you don't own. So is the "engineering" part of this finessing the input and massaging the output? Do most "AI Engineers" actually understand what's going on in that function, beyond what they learned in the "LLM 101" videos and articles that have been flooding the web over the past year? While an application developer doesn't need to know all of the ins-and-outs of the underlying operating systems they run on, those developers that do have a deep understanding of the OS are the ones who write more performant code, and can really get to the bottom of issues that arise do to how their code uses the OS. Can the same be said of "AI Engineers"? All of a sudden, everyone's an AI Engineer. Where where these experts hiding five years ago? https://trends.google.com/trends/explore?date=today%205-y&geo=US&q=blockchain,GPT https://trends.google.com/trends/explore?date=today%205-y&ge...

voz_ 3y ago
Imagine comparing blockchain and AI.
- aerhardt 3y ago
  LLMs and GenAI are already useful at scale but the current hype that they will lead to infinitely generalizable models, myriad groundbreaking applications in all fields and industries, or even AGI could be overblown - let's at least admit that as a possibility.
  - lost_tourist 3y ago
    If AGI reached "human level" capabilities and took off from there how in the world could that possibly be overblown? That would change everything, comparing it to discovering fire would probably be apt, and maybe the first time in recorded history that making that statement isn't overly dramatic.

Der_Einzige 3y ago

If you are someone who was working in LLMs/Generative AI before it got "cool", The market has fundamentally changed and changed extremely in your favor. Talent is extremely scarce and expensive right now in this space. Ask for well above FAANG compensation. Try to shop around for offers. I had 4 offers and a bidding war for my talent (with no leetcode - but I have an extensive github and publications about LLMs), when previously these same companies would have brutally leetcoded me. I thought that there would be a ton of people getting really good really fast with Generative AI. It turns out that most people are terrible at using the tools, and there's even research about this right now - https://dl.acm.org/doi/10.1145/3544548.3581388 https://dl.acm.org/doi/10.1145/3544548.3581388

sanderjd 3y ago
I'm someone who would like to get "really good really fast", but have found that the on-ramps remain pretty weak. For instance, I have yet to find a good book on the subject. There are a ton of tutorials and articles, but it's maddening to try to cobble together any depth of understanding from those little nuggets. And there are tons of good papers on how the systems actually work, but these are not very useful for people who are new to this set of tools to figure out how to use them to create useful things. But I think this will hole will close up incredibly quickly over the next six months. (And it's yet another really good opportunity for people like you to be involved in that gold rush!) Edit to add: For instance, the article lightly lambasts peoples' recommendations to read up on AI / ML fundamentals, and contains this: > n the near future, nobody will recommend starting in AI Engineering by reading Attention is All You Need, just like you do not start driving by reading the schematics for the Ford Model T. But, amazingly, it doesn't actually suggest an alternative starting point*. There is this call to action about the conference, but presumably that will mostly be people who have already figured this out to some degree. But (to carry on the author's analogy) what is the recommendation for driver's ed?
- swyx 3y ago
  we haven't launched it widely yet but if you peek at the top level nav you'll find the course we are working on :) https://www.latent.space/s/university https://www.latent.space/s/university

supportengineer 3y ago

There are a couple types of roles that sounds really interesting to me. One would be taking some proprietary data and training LLM in a format it could use. One company I know has a database of cars. They want to train their LLM with some inventory facts like "We have a Ford Mustang on the lot whose vin is ABC123 and it has the following features...". And then another role would be writing the prompts for API calls to the LLM. "Write a report on all cars currently in the lot which are available for sale and have the following features...."

wredue 3y ago
This doesn’t seem like a thing any business should need AI for. Filtering a known list for a report takes a few seconds at most, and is much more reliable than LLMs.
Jtsummers 3y ago
> They want to train their LLM with some inventory facts like... So are they actually intending to retrain their LLM every time the inventory changes? Because, otherwise, how is it going to "know" the current state of the inventory? This is useless after a single sale or a single new delivery without retraining. (And it's likely useless before that anyways.) And if they already have a database of inventory data with all this then they could just generate a report the "old fashioned" way that's worked for decades.
- wizofaus 3y ago
  I would expect the solution is to take the NL question and get GPT to transform it into a SQL (or similar) statement to extract the data. Then another call (or set of calls) to generate "reports" summarizing the data returned by the DB query.
  - deleted 3y ago
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  - john2x 3y ago
    At which point, maybe a "GUI-interface" will be cheaper to build and maintain in the long run. Now if AI could automagically update inventory data with what's actually physically happening on the lot, that would be cool.

cj 3y ago

I can see a world where "ML Engineer" (or similar) is someone that's hired to solve a known problem (whether it be with classifiers, LLMs, neural nets, etc), whereas a "AI Engineer" (or whatever the title) is hired to figure out how the hell to capitalize on the AI hype, without a specific problem to solve. IMO right now we're entering the "Peak of Inflated Expectations" in Gartner's hype cycle model. https://en.wikipedia.org/wiki/Gartner_hype_cycle#/media/File:Hype-Cycle-General.png https://en.wikipedia.org/wiki/Gartner_hype_cycle#/media/File... Lots of companies want to jump on the AI bandwagon, but they don't really know what to do or how to leverage it. What the LLM community needs now is for companies to leverage and productize these LLM models for truly game changing use cases. If that doesn't happen soon, the hype will start to fade and lose momentum ("trough of disillusionment"). I'd really love to see some killer use cases emerge soon.

yacine_ 3y ago
The right way to leverage AI is to use it to do ye olde engineering, faster. Actually, most AI use cases are in fact that! Ye olde engineering - data pipelines, automation. Stuff we've been doing since before I was born. Everything else - transformative tech, will hit the open domain fairly quickly, is my bet. Similar to databases.
richardw 3y ago
I’ve been thinking the leading edge is already in the trough of disillusionment! At least on HN etc. We see lots of reports of limitations on HN. Those in the know don’t trust it nearly as much as the public and all the CEO’s of businesses drooling to install AI and have the money go up. Going to have to dig our way through those issues to get real value.
- moffkalast 3y ago
  Well you have to consider the inherent bias on HN, in that the trough of disillusionment is HN's default state of mind regarding literally every topic. It's really rare to see a thread where most of the comments aren't negative.

avereveard 3y ago

The rise of the monkey king of infinite monkeys I'm using LLM for many things, but let's not pretend it's engineering

preommr 3y ago

Oh God, this is going to be like DevOps all over again isn't it? Where there's people constantly gluing together things from different companies. I foresee layers and layers of abstraction dependent not on technology spec but on some company's poorly maintained docs and apis that you get by calling some service rep.

sanderjd 3y ago
I have found data engineering to feel that way as well. Just throwing darts at a board of vendor solutions, trying to figure out how to cobble them together into something useful. I am worried this is going to be that as well. I'd like to learn more about how to self-host systems that are large enough to be useful, rather than doing this cobbling together proprietary APIs thing.

paxys 3y ago

I'm not sure I agree with their reasoning. There are a lot of generalist backend/infra engineers who are working in the AI space. What is the special skillset that distinguishes them from all others? If you say that they are "AI engineers" because they are working on AI as a product, then should we also have "advertising engineers", "billing engineers", "API engineers"? The reality is that tons of people just build a trivial app calling an OpenAI API and put "AI engineer" on their resume to capitalize on the hype.

zwaps 3y ago
Tech companies ARE doing that. I have met "Customer Success Engineers" and other managerial or marketing related roles with the moniker Engineer. People want to call themselves engineers.

rufius 3y ago

AI/ML Engineers will be our modern day variant of the Mystic. They'll whisper sweet nothings into the ears of their models, looking at the goat entrails of what their models output and tell us of their predictions. It's going to be a shit show.

amelius 3y ago
This stuff is going to be so vague that an AI will be able to do it.
somewhat_drunk 3y ago
Hilarious!

Zetobal 3y ago

It's not the AI Engineer it's the AI Operator that everyone is looking for.

sanderjd 3y ago
"Operator" feels right to me as well here.
- john2x 3y ago
  Nice. One step closer to having Smooth Operator be a real title

janalsncm 3y ago

I still feel a bit strange about calling someone an “AI engineer” and I think there are a few reasons. 1. AI is poorly defined. This is a fundamental problem behind almost every conversation on the “topic”. Depending on the context, AI can mean anything from a decision tree to deep neural networks to science fiction. 2. Engineering implies a deeper level of understanding. If you want to engineer a system, you need a deeper understanding of how each of the components work. Using a tool does not make one a tool engineer. It makes them a 21st century blacksmith. Does calling an LLM API make me an AI engineer? If so, calling a weather API makes me a weatherman. 3. This is a relatively new area, and make no mistake, it is a new area. 2 years ago most of the tooling around ML work meant you had to get your hands dirty. To use BERT, you pretty much needed to learn about tokenization and attention masks and CUDA. Not so much with GPT3. So it seems premature to even circumscribe at the moment. What I am not saying: I don’t think one needs to take a class in linear algebra to work with this stuff. I also never believed in calculus for CS students, which may be a minority opinion too.

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minimaxir 3y ago

I would never be able to be hired as an "AI Engineer" or "Prompt Engineer" despite my extensive AI portfolio because my job title is Data Scientist and the discrepancy would confuse most hiring managers. Job titles are moving faster than career ladders.

Animats 3y ago

Has "robopsychologist" shown up in job ads yet?

m3kw9 3y ago

AI engineer is a hype term for someone that can incorporate LLM to solve an engineering problem. It isn’t that hard to use OpenAI apis, LLM are like super abstractions. You tell it in natural language, as opposed to a specific programming language. It’s still a very narrow field. The hype is real but it’s getting a bit frothy

__rito__ 3y ago

> "the fundamental gatekeeping that still persists in the market" This person just called me a gatekeeper when just our definitions of what an AI Engineer is different... all while discussing the ambiguities of the term "AI Engineer". Well, that's the internet for you! My intentions were never of gatekeeping, our definitions of the term are wildly different. I laid the beginning part of a roadmap for a person who wants to solve problems that exist in the real world with Deep Learning- by choosing how to structure data, training models with existing architectures (and optimizers, loss functions, activation functions, and so on) and creating new ones from scratch- and to deploy them to solve the problem in the physical space. Although, I have not spent hours on what the term "AI Engineer" should mean, it is not prompt engineering to me. If there is a consensus in future on the term meaning prompt engineering, then I will not give any advice on that, because I don't know much. Neither will I fight the definition. My definition, still is the person who develops new kinds of microwaves as opposed to the author's whose definition is that of a cook who uses the microwave, by creating new recipes and dishes. That's fine, but didn't liked being called a gatekeeper.

swyx 3y ago
apologies for that - it came from a friend who was reviewing that HN thread and used that word - but it was my choice to repeat that word in my writeup. I didnt take your feelings into account when using what I knew to be a loaded word, and in retrospect it did nothing to further the strength of my argument at all. I'm sorry. I've removed it.
- __rito__ 3y ago
  Thank you so much. As I mentioned, my definition was just different. And I didn't even mean to gatekeep. My spirit was: "if I can do it, many more people definitely can, too". So I went on and shared an outline of my journey. That's all. No hard feelings.

charles_f 3y ago

There's a bit of snake oil in all of that. By any means ML is a very specialized subfield, where you need solid math basis and a deep understanding of the science behind it all. But I struggle to see the same thing for AI. If by "AI engineers" you mean someone who builds an LLM*, then it's very much just ML. If you mean someone who integrates with the LLM someone else built, then it's very much just backend work. Sure, you might need a few days to understand a few concepts, I've integrated with Paypal in the past and that doesn't make me a payment for engineer. * wont even get into the argument of labelling LLMs with AI

fragmede 3y ago
Well, and frontend work. ChatGPT wouldn't been anywhere near the product it is without the webterface. As far as PayPal integration, no but it makes you the subject matter expert (SME) in the room above a room full of people who aren't, and maybe aren't even developers.
senko 3y ago
If you have React engineers, why couldn't you have AI engineers? I think the post is right to try to delineate the roles of "builds LLMs or other ML models" and "uses those models", as (as you noted) the skills required are very different (vs. "creates a JS framework" vs "uses a JS framework" are the same skills, just on another level). "AI engineer" is, as you say, inaccurate, but as swyx says, it's least cringy of the alternatives. > Sure, you might need a few days to understand a few concepts, I've integrated with Paypal in the past and that doesn't make me a payment for engineer. No, but the post explicitly calls out the breadth of knowledge that the future AI engineers will have to have. It's not something you can pick up in a few days, even now, if you want to be on top of things. I can look up some docs and hook up static file hosting on S3 and that doesn't make an AWS engineer. But there are people who full time work on providing solutions using AWS-everything and have built their entire careers (and companies) on top of that specialization. The difference in scale is the difference in job description.
- ignoramous 3y ago
  > If you have React engineers, why couldn't you have AI engineers? We most definitely could. That's one way to prop up a cottage industry, stay relevant, command hefty salaries, and maintain that charade of importance through complexity by creating Frankenstein features in search of problems and promotions.

vjust 3y ago

recently saw a demo app built on top of GPT3. Used a rest API for prompts. The backend was hooked to a corpus of PDFs and a SQL Database with financial data. What changed was the queries were English/natural language. The query language has changed. That's the R in CRUD. I wonder if the C, U, and D will also change. While this is one of many types of AI, it means commands will be in natural English. This might be a big deal for UI builders because how we ask questions has changed to NL.

wizofaus 3y ago
I'm curious about this sort of use case - how long does it take for a GPT-based system to process a bunch of documents it's never seen before so that you can perform NL searches on them? Assuming something ike a million total pages of text are we talking minutes? Hours? Days? And is it at all feasible to ensure whatever factual information is returned is only sourced from said documents, vs being "hallucinated" by virtue of whatever weights exist based on the core training corpus?

amelius 3y ago

AI engineer is the new webdeveloper.

intellectronica 3y ago

In my experience people who make dismissive comments about AI (in its current form) and AI Engineering as a discipline tend to have very little experience with and superficial understanding of AI. Once you start using it seriously as part of your engineering stack it quickly becomes clear that there’s a lot of detail and complexity that more than justify specialisation.

z3c0 3y ago
Heavily disagree, and this just seems like posturing from the other side of things. I understand AI rather heavily, and have been a part of the scene far before the explosion of even the first GPT model. Frankly, I've been quite irked by all the people who claim themselves "ML engineer" when most of what they do is glue huggingface models together. They're doing none of the work required to make models, which is quite extensive. They understand very little about the models they wield. This new wave, I find even more irksome. "Prompt engineer" and "AI engineer" and aren't much more than pseudoscientists who fanangle API inputs until they get what they want. This is in stark contrast to all the maths I perform to understand a model's performance.
- intellectronica 3y ago
  I think you've just proven my point.
  - z3c0 3y ago
    On reddit, this might seem like a burn, but here, you need to explain that claim and not just hope somebody rides with it. How is tinkering with an API an actual understanding of AI? Please actually explain
- foobiekr 3y ago
  Strong agreement. What we have right now is a lot of people doing light glue code and pretending it is engineering.
  - 3y ago

boredumb 3y ago

I think it's all just being more accessible for people like me to integrate into applications via AIs. With that said - the barrier for entry also lowered substantially in the last few years to a point where implementing your own neural network from scratch with Pytorch is something you can do in a few (took me a weekend) hours coming in blind, but reading the docs and scrolling some existing repositories. I remember a decade ago taking a stab at it and being very much over my head. Not sure if i'm getting older and there's less magic going on around it or if it's the ecosystem/docs/examples/pytorch/jax/hours of youtube content/etc but somewhere in there, at least for me personally, it's gotten much more accessible.

amilios 3y ago
Personally I think it's a combination of a) much better resources being available than in the past, which explain concepts clearly and in relatively simple terms, and b) much better tooling existing now than ever before, so many things that you used to need to do "by hand" are now taken care of by relatively standardized tooling. Even just automatic differentiation engines are a huge deal, not having to backprop "by hand" (first release of TensorFlow was 7 years ago, so if you looked at it a decade ago I'm assuming you had to implement backprop yourself). Beyond that another jump was from Tensorflow's delayed execution/compilation model (it really was a headache to work around its APIs/set up the computation graph) as well as just having a generally ugly API, to PyTorch's "literal" setup, where it feels like you're just writing regular Python code and performing operations "normally"

ilaksh 3y ago

I mean, I consider myself an effective "AI Integration Engineer" but I _have_ done an Andrew Ng ML Coursera course and also built a MLP in C++ from scratch. But none of that really matters when it comes to applying something like GPT or Stable Diffusion to a particular application. You just send appropriate text to a model via an API call. I think "AI Integration Engineer" is a bit more of an accurate title because usually it's about integrating AI into existing products or domains. And "AI Engineer" by itself sounds a little bit like you might be claiming to be a PhD. But just a bit. I think it's fair enough to shorten it to AI Engineer though so we don't all have to type out "integration" over and over.

spaintech 3y ago

My thought on what an AI engineer is; an individual who uses AI and machine learning techniques to develop applications and systems that can help organizations increase efficiency, reduce costs, increase profits, and make better business decisions 1 . AI engineers play a crucial role in helping enterprises leverage the capabilities of large language models (LLMs) like GPT-3 and beyond, this means that they will, “Develop Domain-Specific Models” AI engineers can fine-tune LLMs to create domain-specific models to ensure that the data and the business process align to provide more context. For example, a model fine-tuned on medical literature can assist doctors in diagnosing diseases or answering patient queries, ect. “Data Preparation and Management” By this, I presume they will be involved in cleaning the data, dealing with missing or inconsistent data, and ensuring the data is representative of the task the model will be performing. “Integration with Existing Systems” they will help integrate these AI models into the existing IT infrastructure of an enterprise. This can involve developing APIs, designing user interfaces, and ensuring the model's outputs can be used by other systems or processes. Etc… I believe that they enable organizations transform data into knowledge and ultimately, into wisdom - the highest level of data maturity. This is particularly true when dealing with domain-specific models, which can provide highly targeted and context-specific insights. When you can reach a level of data maturity that enbles actions on data, this is where AI will drive the change that is just starting. It’s very exciting to watch it unravel, not because of the possibility of generating a sentient machine, but with the possibility that will drive new fields of discovery that has been under our noses, and these tools will help us make sence of it all, IMHO.

Uptrenda 3y ago

Tl; dr: a bunch of know-nothing plumbers swerve their digital cars from last years buzzword (blockchain) towards AI, narrowly missing a collision with a crowd of VCs. Upon arriving at the AI convention they switch hats to AI where they list themselves as experts (they can use the Chat-GPT API.) How any of this works or what problems may arise are a job for the cleanup crew (paid less, they aren't calling themselves experts.)

Sai_ 3y ago
This is unnecessarily combative though it’s also probable I’m reading too much into this since I’m one of those swervers - from cloud computing guru to AI aficionado. Personally, my mental bent is towards tinkering - I like wiring things up to make them work. I used to do that with wires and screwdrivers as a kid, now I read api docs and do the same thing. The point being, it’s likely that I’ll continue connecting things to each other just to see how they work and offer that as a service until the day I die. You need people who invent the algos, people to wire up algos to each other, people to sell this service to people, people to want to pay for that service, and so on. None is better than the other. They’re all necessary for people to put food on the table and live the life they want. Sneering at them is pointless.

mercurialsolo 3y ago

With the rise of LLM's there is definitely a new skillset needed in the bag of engineers - one who know how to align/instruct LLM's towards an objective. This new role is essentially a mix of standard programming logic + ability to instruct LLM's using language (natural language programming). That's been the primary difference I have seen in this emergent field of designing agents. Most agents need some form of formal instructions tied with designing prompts - which are a mix between formal programming and natural language. This needs a slightly different skill set from the traditional backend engineer or services engineer. This is not a ML or data role either. Rather than just a prompt engineer, the case for which could be made like a specialist designer - this role maybe more akin to a full-stack developer role who knows how to align LLM's or Diffusion models towards a multi-step goal.

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