Ask HN: How's the current state of hiring in the LLM field?
How are the hiring trends in the LLM field currently?
Do you think they will grow in the coming years like it did for data scientists?
Is it worth entering the field today starting from scratch?
75 comments
[ 2.9 ms ] story [ 153 ms ] threadOn the other hand, only a small amount of companies have the money, interest to actually at least finetune LLMs. They have their people already in place.
Than you have people who should integrate LLMs and tbh thats something everyone can do who was able to integrate any other api. I don't think there will be middle class companies looking for LLM experts.
You couldn’t be more wrong. Such people make 7 digit salaries, either at FAANGs or at unicorn startups. It’s the hottest job in ML today. Though very few people can actually build a GPT-5 level model.
- UX improvement / service delivery
- Reliability / QA
- Implementation
In most cases business just want to use an LLM but don't need any expertise in how they work. 80% of use cases are "I want to write an English sentence to make a SQL query". With a little bit of fine tuning or RAG you get there.
Being a data scientist who understands LLMs would be far more likely to be a good career move.
I use LLMs every day for code assistance so I'm not totally naive to the whole thing, but I seriously wonder what direction to take my career, exactly. Should I be going all-in on learning CUDA? Is knowing how to wire up a few API calls enough for "soft" applications? Should I in fact be keeping up with the latest RAG techniques (which seem to change every time a new foundational model gets released)? Should I just stay out of the whole thing and double down on classical ML applications in my domain?
Thank you. Both questions are what I'm wondering right now.
I'm pursuing of a strategy of:
* Doubling down on domain knowledge. Even if coding becomes completely automated, there will always be value in knowing some particular corner of an industry very well. For me, that's insurance. This includes knowing how classical ML techniques can be applied in my industry (LLMs are not replacing xgboost any time soon).
* Keeping tabs on bleeding edge LLM capabilities, just to have a sense of what's possible. However, staying out of highly technical LLM implementation details like CUDA, inference, batching, etc. Perhaps fine tuning will be valuable but probably I would focus on understanding "fine tuning as a service" offerings rather than rolling my own.
* Investing in knowledge of infrastructure/orchestration of LLM services in a way that doesn't tie me to any particular problem space. It's hard to predict right now what kinds of new products LLMs might enable. But I think there will be value in being able to translate business problems into a high level architecture for an LLM-enabled app, even if that just means wiring together existing service offerings.
Thanks for your input
Maybe, but for every haystack that an LLM can search, RAG could search an even larger one. I don't think RAG is a killer app but I think tools that help LLMs find the right context will continue to be useful.
> Doubling down on domain knowledge
I'm a dev, but this is my main strategy right now. I am digging deep into my corner of fintech and hoping that will stave off some of the disruption I see coming for tech. Personally, I don't see why an AI couldn't also become a domain expert in my niche, but it should buy me some time and potentially align me with some powerful stakeholders (portfolio managers just need to convince the big money that a human touch is good and I will still be business).
Question: in what ways does insurance domain knowledge set you apart?
I understand certain industries benefit from Engineers with domain experience (e.g. Healthtech companies needing HIPAA knowledge).
I'm curious as to what particular skills insurance companies need from Engineers?
* Rating (a deep, classical, tabular ML problem) is still catching up to SOTA techniques in many insurance companies + uneven regulatory environment makes it tricky to navigate.
* Marketing is another classical ML problem, the goal is to optimize ad/lead broker spend/conversion funnel.
* CS of course has huge opportunity with LLMs.
* Automated claim fraud detection is a very difficult problem amenable to LLMs/MM DL models. Lots of "pivot to AI" going on here.
And there are others of course. The main thing is that insurance companies have limits on product innovation - policy terms are in many cases mandated by law, so the main differentiator is operational efficiency and exellency. Solutions that move the needle on big revenue/cost levers are very valuable and can be applied widely.
same. future looks very uncertain. on one hand, the new figure of "ai-assisted x" where x can range from developer to copywriter to a whealth of other primarily creative job will replace the non ai assisted variants due of the tangible increase in productivity
on the other hand the reduction in these cost possibly means 5x less capital requirement to build a startup, whose primary cost today is personell, at least in their early stages.
wether will that increase in company creation be enough to raise the demand for personell to a level where the society is stable I don't really know.
the primary risk I see is not from career prospet but from the massive capital investment that is required to be in the club of model providers, these will be the new elite, with unparallel power over economies and entire states. if you think the robber baron period was harsh, wait until three companies are ingrained in every aspect of your life from bureoucracy to healthcare and having to have to argue with a self moderated model instead of a bureaucrat, and if that's not enough imagine that maybe you have a uncommon or suggestive name and the model won't believe you no matter what proof you might give it. imagine these model being part one way or another in the production of entartainment, generating a monoculture that is extremely prude and milquetoast. or in general the model being wrong about anything really, you cannot reason with them.
my concern is that there's a lot that should be happening right now at a legislative level, not in term of stifling innovation, but let's say putting certain role outside of the sole control of AI, which it isn't, and if we wait to be reactive about it, it's gonna end up like it did for self drivin: blood on the road and I cannot have my car acting as a taxi and earning for me during off hours because for safety reason only the tech owner can profit from it, yeah, right.
those panning for gold (app devs, startups etc) may or may not find it. remains to be seen and i remain skeptical.
As to whether that rebrand itself is successful, I don't know. It feels like the DS role is undergoing a maturation where different skillsets are being cleaved into different roles. I was always a more natural fit for the Ops side, I don't have the academic creds for a research-oriented position. But I'm not sure how much of ops means "LLM ops".
Can you elaborate on this?
Whether they will continue to grow is hard to say. Putting open models to productive use is difficult. Like stable diffusion can make a pretty image but can it make the image you want? For example, can it make a 2d video game sprite, can it make the same character consistently in different poses.
That's a personal project problem I have but it is the same vibe as LLMs with code. Like they can produce some code, but a more complex algorithm or larger program is tough. I'm not sure if there will be an upper limit on what they can do. Right now it is a lot of engineering to get them to do stuff with no human in the loop.
Not a lot of companies are training models. There are probably a number that are trying to integrate OpenAI's API in some way.
They can do powerful things and it is a good skill to learn, but it feels like the market right now is for PhDs from top schools or low paid data labelers. Maybe others will have different experience.
For resources, check out [1], [2], and [3]. The third being my least favorite, but others like it.
1. https://karpathy.ai/zero-to-hero.html
2. https://deeplearning.ai
3. https://fast.ai
Could you provide more information about the knowledge and experience your company seeks for that position? You mentioned it's challenging to find individuals with certain skills. Could you elaborate on what those skills are?
A handful of companies are doing LLMs. Rest are doing internal fine tuning of available models.
LLMs have proved themselves not to be that reliable that they can be left in production alone.
Hence is the adoption. Limited to areas where they don't have significant impact such as "Change the tone , rewrite, summarize" context menus in many products.
Here's a personal anecdote: I'm only really good / competent in technical areas that truly interest me.
In technical areas that I've pursued only for career / money reasons, my performance has been sub-par. Especially when compared to that of developers who are truly interested.
I don't really know how much my experience generalizes to other developers. Maybe it's related to my ADD.
ADHD is as real as diabetes or arthritis. If that disturbs you, it says more about you than it does about ADHD.
I think folks are conveniently lying to themselves, and I'm not here to ruin anyone's party but I do get concerned in parenting circles when this kind of over-optimizing of normal laziness gets sold as childhood ADHD.
So yea, it’s weird
And ironically, this is not going to be a marketable skill for long, in any scenario. If the hype train crashes and burns, this is no longer a useful skill. But if the hype is real, an ML model will be able to do this sort of thing for you.
What's missing is any novel insight and real high quality writing. I guess what I'm trying to say is if LLM development totally stopped tomorrow I would still use it for generating a bunch of ideas and volume of writing to then pick from.
It's like being an editor vs being an author, and tweaking text output is easier than starting from scratch.
OTOH ML models seldom "do" much of anything on their own, it needs integration work to make it matter.
I have been saying I'm a "software engineer with a recent focus on generative AI". Half of my clients are convinced that they need to train a custom model in order to solve their problems at all. Because they typed a request into ChatGPT and it didn't do exactly what they wanted. But usually if I just do an API call with an actual system message and temperature 0 then I can demonstrate that they don't need a custom model.
There are good reasons to want custom models, but those are probably phase two or three of most projects. And 100 X as expensive and complex. Phase one for many projects will work fine as a proof of concept using an API call or two.
I think there will be jobs for ML specialists that know how to do fine-tuning of common models. But you will be competing with people like me who don't have an ML PhD. But beyond that, it seems that you have to build specialized architectures that can compete with the general ML architectures built by leading edge researchers at huge companies. And it's becoming increasingly difficult to invent a new architecture that is truly better in a niche.
Having worked with LLMs a lot I would NOT agree with people who call them "just another tool". Some people are experts working with cryptography, or creating scalable distributed systems, or working on network protocols, etc... these are real and specialized skills. Working with LLMs is similar. Starting from a foundation of "can get stuff working" (full stack) is awfully important, and I don't think LLMs reward narrow specialization.
Also prompt engineering is real, and I don't think it's going anywhere. Working in collaboration with domain experts to do that prompting is essential, but a lot of output issues benefit from a combination of both prompting and changes to pipeline, text processing, and other code-based approaches. There's real benefit to being good with words, a close reader, able to get in the head of the LLM, learning its fixations and misconceptions, templating thought processes, etc. Ideas that prompt engineering will disappear with better models and fine tuning are, IMHO, naive and misunderstand the interplay of prompt and LLM. If you want to get something from an LLM you will still need to know how to ask!
customer-service, code-assist, call-center are a few areas which show early promise wherein customers are willing to pay for the added value. outside of these areas, i am yet to see breakthrough applications for which people are willing to pay. let me know if this is mistaken.
Any software will just be created without any care for frameworks, platform, hosting, etc... it'll be hosted in the 'brain' of the ai.
I'm really not sure about that... seeing people talking about LLMops (as a role) and things like that. Well, let's see.
…but not sure that necessarily makes it a good foundation for a CS career. Especially with much of this moving towards almost no code prompting territory
Job requirements are for applied researchers.
Job work is Excel/OpenAI wrangling.
Maybe. But then the demand might also shrink quickly after a couple of years. Is it worth that risk? Only you can decide.
I'm yet to see a truly strong LLM application where the results are not significantly better than a smart person with chatGPT open and a bit of patience to craft some prompts.
What are the most impressive things available that are not just chatGPT?
I'd also like a way to take a printify listing, and create duplicates for variations, like design 1 on a unisex tee, unisex hoodie, toddler tee, kids tee, and baby onesie - its very tedious to do this now...
Some of this I can do via python or php and api's, but current workflow is Dalle-3 on bing -> canva for fixes (bg removal and upscaling) -> {printify -> etsy}|{etsy} ...with some gpt back and forth for title, tags, description.
If I can say "create a pumpkin pie vector graphic in the shape of pacman for thanksgiving party decor" and have it push title, tags, picture (upscaled picture) to etsy on step 2...that saves me like 15 minutes or more per listing.