Still not clear to me what is meant by "ai" now? My sense is that it is a marketing term for LLM. Is that accurate? Do people now consider any ML project to be ai?
That's what I was alluding to, I don't think it defines ai, do you? These pieces seem like classical ML pieces to me plus LLM. Is that ai? Like from a technical standpoint, is it clearly defined?
Honestly it's a large enough library with enough weirdness and untested areas, footguns, and bugs that I'd deem it just as valid as React for example.
Why did tensor_parallel have output += mod instead of output = output + mod? (The += breaks backprop). Nobody tested it! A user had to notice it was broken and make a PR!
> Even when you live and breathe AI, the job titles can feel like a moving target. I can only imagine how mystifying they must be to everyone else.
> Because the field is actively evolving, the language we use keeps changing. Brand new titles appear overnight or, worse, one term means three different things at three different companies.
How can you write that and not realise “maybe this is all made up bullshit and everyone is pulling titles out of their asses to make themselves look more important and knowledgeable than they really are, thus I shouldn’t really be wasting my time giving the subject any credence”? If you’re all in on the field and can’t keep up, why should anyone else care?
I agree some analysis of job postings or pay distributions by title would’ve made this article stronger. The titles are less relevant than the job descriptions, which are task specific and not bullshit.
I thought this was going to be satire. Software engineer job titles are already pretty bogus (Senior Principal Distinguished Engineer, anyone?), and the AI trend has only created more jobs with nebulous descriptions around "doing AI".
I assume if you are applying to AI roles, you use AI to find and possibly apply for you. So, we don't even need to understand what the titles mean because AI can do it for us.
They could just call it "Field Service Tech" like the rest of the universe. I understand using title inflation/deflation to keep pushing the engineer title (and pay expectation) into the dirt, but still, this is dumb.
I hope AI attracts all the people I hate to work with the most in software engineering. The pretenders, the hype chasers, the people looking for money, the ladder climbers. I hope they all become AI engineers and leave our profession alone.
Some already became "data scientists" and "ML engineers", I hope this AI wave takes the rest.
Who cares about this bs. As soon as the bubble bursts, the job title would become 'unemployed'. And if they are too successful and AI takes over their job, the job title will be 'unemployed'.
Their current title: 'overpaid bot therapist' or 'the prompt whisperer'. What a load of bull.
Seems about right. My official title at work is "AI Engineer". What does that mean exactly?
- I'm not a researcher and not fine tuning or deploying models on GPUs
- I have a math/traditional ML background, but my explanation of how transformers, tokenizers, etc work would be hand-wavy at best.
- I'm a "regular engineer" in the sense I'm following many of the standard SWE/SDLC practices in my org.
- I'm exclusively focused on building AI features for our product, I wear a PM hat too.
- I'm pretty tuned in to the latest model releases and capabilities of frontier models, and consider being able to articulate that information part of my job.
- I also use AI heavily to produce code, which is helpfully a pretty good way to get a sense for model capabilities.
Do I deserve a special job title...maybe? I think there's definitely an argument that "AI Engineering" really isn't a special thing, and considering how much of my day to day is pure integration work with the actual product, I can see that. OTOH, part of my job and my value at work is very product based. I pay a lot of attention to what other people in the industry are doing, new model releases, and how others are building things, since it's such a new area and there's no "standard playbook" yet for many things.
I actually quite enjoy it since there's a ton of opportunity to be creative. When AI first started becoming big I thought about doing the other direction - leveraging my math/ML background to get deeper into GPUs and MLOps/research-lite kind of work. Instead I went in a more producty direction, which I don't regret yet.
The author’s definitions suggest you should have “Applied” in your title, which I like, but my impression is that “applied” roles so vastly outnumber “creation of models” roles globally that it’s actually the latter that would benefit from a modifier. For now, you have to rely on context (mostly the nature of the company’s primary output) when trying to interpret something like a job posting or an acquaintance’s title.
It’s not that crazy to add a couple of domain-specific prediction heads to a BERT-family pretrained model and then do a quick fine tuning. By volume that’s less common but I would guess most people are just using things off the shelf and might not even consider themselves AI engineers. I have no frame of reference though.
We all know the AI part is largely meaningless because of the hype and nonsense, but what defines you as an engineer? When you consider that classical engineers are responsible for the correctness of their work, combining it with AI seems like a joke
> "When you consider that classical engineers are responsible for the correctness of their work"
Woah hang on, I think this betrays a severe misunderstanding of what engineers do.
FWIW I was trained as a classical engineer (mechanical), but pretty much just write code these days. But I did have a past life as a not-SWE.
Most classical engineering fields deal with probabilistic system components all of the time. In fact I'd go as far as to say that inability to deal with probabilistic components is disqualifying from many engineering endeavors.
Process engineers for example have to account for human error rates. On a given production line with humans in a loop, the operators will sometimes screw up. Designing systems to detect these errors (which are highly probabilistic!), mitigate them, and reduce the occurrence rates of such errors is a huge part of the job.
Likewise even for regular mechanical engineers, there are probabilistic variances in manufacturing tolerances. Your specs are always given with confidence intervals (this metal sheet is 1mm thick +- 0.05mm) because of this. All of the designs you work on specifically account for this (hence safety margins!). The ways in which these probabilities combine and interact is a serious field of study.
Software engineering is unlike traditional engineering disciplines in that for most of its lifetime it's had the luxury of purely deterministic expectations. This is not true in nearly every other type of engineering.
If anything the advent of ML has introduced this element to software, and the ability to actually work with probabilistic outcomes is what separates those who are serious about this stuff vs. demoware hot air blowers.
Hard to tell what you're even trying to say here. I am obviously responsible for the correctness of my work. "AI Engineer" does not generally mean "AI-Assisted Engineer", thought that was clear from my post.
Who cares? The word "engineer" is meaningless now and anyone can be a self-proclaimed engineer whenever they feel like it. Will anyone double check or even reject you for an engineering job when you are not? Absolutely not! Take a bootcamp, submit plenty of PRs correcting typos, and pass the interview with the help of AI and you basically made it, dreams come true!!
> what do you think of recent MIT news that 95% gen ai projects don't do anything valuable at all ?
Worth noting that a project that ends up “doing nothing” isn’t the same as a project that had/created no value.
Even some projects that in hindsight were deterministic lemons.
Assuming compute resources continue scaling up, and architectures keep improving, AI change now has an everything, everywhere, all the time, scope. Failing fast is necessarily going to have a substantial dimension.
Almost nobody here wanted to be an 'AI researcher' until late 2022 when the money started pouring into AI researchers.
Now with this article clearly defining each of these roles (AI researcher being the most serious out of the rest) everyone now suddenly wants to be one.
"AI" is a vast field which spans beyond deep learning and LLMs. Unless you are very serious and fully interested in actually advancing the field, don't bother.
Why not robotics or electrical engineer? Not cool enough?
Still not clear to me what is meant by "ai" now? My sense is that it is a marketing term for LLM. Is that accurate? Do people now consider any ML project to be ai?
Statistics - stuff I can do in Excel as long as no one asks for an underlying proof involving integration.
Machine Learning - stuff I apply with some understanding.
AI - stuff I apply without understanding.
It means "hey investors, we're worth giving money to, we promise!"
What is meant by "computing" now? Computers used to be ladies sitting at giant calculators.
You should read the post. You might find the “domain” discussion interesting.
That's what I was alluding to, I don't think it defines ai, do you? These pieces seem like classical ML pieces to me plus LLM. Is that ai? Like from a technical standpoint, is it clearly defined?
5 replies →
My heuristic has been
ML engineer => knows pytorch
AI engineer => knows huggingface
Researcher => implements papers
I know these heuristics are imperfect but I call myself an MLE because it’s closest to my skillset.
I saw "Hugginface" listed alongside C++, React, and SQL as skills on a resume recently. Wasn't quite sure what to make of that.
Honestly it's a large enough library with enough weirdness and untested areas, footguns, and bugs that I'd deem it just as valid as React for example.
Why did tensor_parallel have output += mod instead of output = output + mod? (The += breaks backprop). Nobody tested it! A user had to notice it was broken and make a PR!
2 replies →
Researcher = WRITES papers.
> Even when you live and breathe AI, the job titles can feel like a moving target. I can only imagine how mystifying they must be to everyone else.
> Because the field is actively evolving, the language we use keeps changing. Brand new titles appear overnight or, worse, one term means three different things at three different companies.
How can you write that and not realise “maybe this is all made up bullshit and everyone is pulling titles out of their asses to make themselves look more important and knowledgeable than they really are, thus I shouldn’t really be wasting my time giving the subject any credence”? If you’re all in on the field and can’t keep up, why should anyone else care?
I agree some analysis of job postings or pay distributions by title would’ve made this article stronger. The titles are less relevant than the job descriptions, which are task specific and not bullshit.
Should’ve included “Member of Technical Staff”
I thought this was going to be satire. Software engineer job titles are already pretty bogus (Senior Principal Distinguished Engineer, anyone?), and the AI trend has only created more jobs with nebulous descriptions around "doing AI".
Senior Anything-But-C-Level-Compensation-Package Engineer.
I saw an "Exalted engineer" once, not kidding.
you might not be kidding, hopefully they were.
when you decide titles don't matter and let people choose their own, you get some titles that weren't created in total seriousness.
I wanna just be a webmaster again.
I think you mean webmain.
How about promptmaster? ;)
1 reply →
I'm still one
Yeah, same. I was actually disappointed when I saw that they were taking the titles seriously
I assume if you are applying to AI roles, you use AI to find and possibly apply for you. So, we don't even need to understand what the titles mean because AI can do it for us.
I'm tempted to use /s, but then again...
"Forward Deployed Engineer" is a bodyshop with LLM.
Weell, you probably don't want to serve "Backwards Deployed Engineers" to your clients
Pretty sure this title came from Palentir who got it from the military.
"Forward Deployed Software Engineer - This role includes working in locations that include risks of getting shot and possibly killed"
1 reply →
They could just call it "Field Service Tech" like the rest of the universe. I understand using title inflation/deflation to keep pushing the engineer title (and pay expectation) into the dirt, but still, this is dumb.
I also dislike the term. It feels concocted to evoke “tacticool” vibes.
Unless you’re pushing new firmware onto a drone in Ukraine, FDE is stolen valor.
2 replies →
What happened to Neural Networks?
I hope AI attracts all the people I hate to work with the most in software engineering. The pretenders, the hype chasers, the people looking for money, the ladder climbers. I hope they all become AI engineers and leave our profession alone.
Some already became "data scientists" and "ML engineers", I hope this AI wave takes the rest.
Who cares about this bs. As soon as the bubble bursts, the job title would become 'unemployed'. And if they are too successful and AI takes over their job, the job title will be 'unemployed'.
Their current title: 'overpaid bot therapist' or 'the prompt whisperer'. What a load of bull.
Seems about right. My official title at work is "AI Engineer". What does that mean exactly?
- I'm not a researcher and not fine tuning or deploying models on GPUs
- I have a math/traditional ML background, but my explanation of how transformers, tokenizers, etc work would be hand-wavy at best.
- I'm a "regular engineer" in the sense I'm following many of the standard SWE/SDLC practices in my org.
- I'm exclusively focused on building AI features for our product, I wear a PM hat too.
- I'm pretty tuned in to the latest model releases and capabilities of frontier models, and consider being able to articulate that information part of my job.
- I also use AI heavily to produce code, which is helpfully a pretty good way to get a sense for model capabilities.
Do I deserve a special job title...maybe? I think there's definitely an argument that "AI Engineering" really isn't a special thing, and considering how much of my day to day is pure integration work with the actual product, I can see that. OTOH, part of my job and my value at work is very product based. I pay a lot of attention to what other people in the industry are doing, new model releases, and how others are building things, since it's such a new area and there's no "standard playbook" yet for many things.
I actually quite enjoy it since there's a ton of opportunity to be creative. When AI first started becoming big I thought about doing the other direction - leveraging my math/ML background to get deeper into GPUs and MLOps/research-lite kind of work. Instead I went in a more producty direction, which I don't regret yet.
The author’s definitions suggest you should have “Applied” in your title, which I like, but my impression is that “applied” roles so vastly outnumber “creation of models” roles globally that it’s actually the latter that would benefit from a modifier. For now, you have to rely on context (mostly the nature of the company’s primary output) when trying to interpret something like a job posting or an acquaintance’s title.
It’s not that crazy to add a couple of domain-specific prediction heads to a BERT-family pretrained model and then do a quick fine tuning. By volume that’s less common but I would guess most people are just using things off the shelf and might not even consider themselves AI engineers. I have no frame of reference though.
We all know the AI part is largely meaningless because of the hype and nonsense, but what defines you as an engineer? When you consider that classical engineers are responsible for the correctness of their work, combining it with AI seems like a joke
> "When you consider that classical engineers are responsible for the correctness of their work"
Woah hang on, I think this betrays a severe misunderstanding of what engineers do.
FWIW I was trained as a classical engineer (mechanical), but pretty much just write code these days. But I did have a past life as a not-SWE.
Most classical engineering fields deal with probabilistic system components all of the time. In fact I'd go as far as to say that inability to deal with probabilistic components is disqualifying from many engineering endeavors.
Process engineers for example have to account for human error rates. On a given production line with humans in a loop, the operators will sometimes screw up. Designing systems to detect these errors (which are highly probabilistic!), mitigate them, and reduce the occurrence rates of such errors is a huge part of the job.
Likewise even for regular mechanical engineers, there are probabilistic variances in manufacturing tolerances. Your specs are always given with confidence intervals (this metal sheet is 1mm thick +- 0.05mm) because of this. All of the designs you work on specifically account for this (hence safety margins!). The ways in which these probabilities combine and interact is a serious field of study.
Software engineering is unlike traditional engineering disciplines in that for most of its lifetime it's had the luxury of purely deterministic expectations. This is not true in nearly every other type of engineering.
If anything the advent of ML has introduced this element to software, and the ability to actually work with probabilistic outcomes is what separates those who are serious about this stuff vs. demoware hot air blowers.
8 replies →
Hard to tell what you're even trying to say here. I am obviously responsible for the correctness of my work. "AI Engineer" does not generally mean "AI-Assisted Engineer", thought that was clear from my post.
Who cares? The word "engineer" is meaningless now and anyone can be a self-proclaimed engineer whenever they feel like it. Will anyone double check or even reject you for an engineering job when you are not? Absolutely not! Take a bootcamp, submit plenty of PRs correcting typos, and pass the interview with the help of AI and you basically made it, dreams come true!!
Yeah the term "engineer" has been diluted into oblivion, and we only have ourselves to blame for not protecting it.
6 replies →
> I pay a lot of attention to what other people in the industry are doing, new model releases, and how others are building things,
what do you think of recent MIT news that 95% gen ai projects don't do anything valuable at all ?
> what do you think of recent MIT news that 95% gen ai projects don't do anything valuable at all ?
Worth noting that a project that ends up “doing nothing” isn’t the same as a project that had/created no value.
Even some projects that in hindsight were deterministic lemons.
Assuming compute resources continue scaling up, and architectures keep improving, AI change now has an everything, everywhere, all the time, scope. Failing fast is necessarily going to have a substantial dimension.
You would need to compare that to the baseline value creation from non-AI projects.
Not sure the majority of projects do anything valuable at all.
Sounds kind of aggressive but probably the number is up there.
Almost nobody here wanted to be an 'AI researcher' until late 2022 when the money started pouring into AI researchers.
Now with this article clearly defining each of these roles (AI researcher being the most serious out of the rest) everyone now suddenly wants to be one.
"AI" is a vast field which spans beyond deep learning and LLMs. Unless you are very serious and fully interested in actually advancing the field, don't bother.
Why not robotics or electrical engineer? Not cool enough?