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Ask HN: Has anyone made the transition from app development to ML/AI work?

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Re: Ask HN: Has anyone made the transition from app development to ML/AI work?

#51

Post by bluefirebrand »

znq wrote: post #50
echelon wrote: post #13

Quit my job in Fintech to work on my own AI startup. I built https://FakeYou.com as a side project, and it blew up. I quit my job after I realized the potential, added monetization, and started to broaden what we do. I've been working on https://storyteller.ai for a year and plan to launch our platform soon. Both of these tool sets reinforce one another. I'm hiring folks that were engineers that want to do AI instead…

What do you mean with "it blew up"? It sounds kind of negative, but from the next sentence it sounds positive. I'm confused.

"blew up" can be positive or negative depending on context. Generally when talking about a physical object it's negative, when talking about a personal endeavor it's generally positive.

"My server blew up" is likely negative.

"My youtube channel blew up" is likely positive, it means it went viral.

If a business blew up, it's almost certainly positive.

Re: Ask HN: Has anyone made the transition from app development to ML/AI work?

#52

Post by znq »

bluefirebrand wrote: post #51
znq wrote: post #50

Earlier quoted context omitted.

What do you mean with "it blew up"? It sounds kind of negative, but from the next sentence it sounds positive. I'm confused.

"blew up" can be positive or negative depending on context. Generally when talking about a physical object it's negative, when talking about a personal endeavor it's generally positive. "My server blew up" is likely negative. "My youtube channel blew up" is likely positive, it means it went viral. If a business blew up, it's almost certainly positive.

Thanks! Appreciate the lesson :-)

Re: Ask HN: Has anyone made the transition from app development to ML/AI work?

#53

Post by stillblue »

lmeyerov wrote: post #24

One concrete example we're hiring in: We're looking for a cleared security / SIEM engineer in Australia to help build out GPU/AI SOC tech. As long as they love Python and modern bits like scaling detection engineering & automation, they'll be learning the GPU & AI side pretty darn fast :)

Is this an internationally remote position?

Re: Ask HN: Has anyone made the transition from app development to ML/AI work?

#54

Post by theGnuMe »

quickthrower2 wrote: post #38
closeparen wrote: post #25

Earlier quoted context omitted.

It's about the same tier as BI dashboards. Huge breadths of data you don't produce but are nevertheless accountable for. All your stakeholders can easily stream-of-consciousness rattle off dozens of new metric/feature ideas or questions about the feature/metric values they're seeing over the course of 45 seconds, each one taking you tens of hours eyeballs deep in SQL to even begin to answer.

A job we would be glad to have replace by an AI for sure

That is certainly coming soon.

Re: Ask HN: Has anyone made the transition from app development to ML/AI work?

#55

Post by throwawaybbq1 »

SCUSKU wrote: post #30

I just made the transition in the other direction. I did a Masters in computer vision, then worked at a startup doing computer vision and machine learning work for 2 years. I recently transitioned into app dev. There are 2 levels to ML/AI, being a researcher and being an engineer. The researcher actually creates new models, architectures, etc. You're going to need to be talented at math, as well as pursue a PhD to ha…

Thank you for your post. Two questions for you:

1) Did your Masters cover non-deep-learning vision (classical vision?) in sufficient detail? There is a ton of math in there. Going from being a shallow user of OpenCV to a deep one seems a big jump. I'm not sure a Masters focused solely on classical vision would get someone there (let alone one covering other things like ML, DL, etc.).

2) Did you end up training large models from scratch or is it all just fine-tuning? I am trying to do the former and I realize getting things to scale for from-scratch training is a whole other topic. I suspect getting things ready for inference would be similar.

Thx!

Re: Ask HN: Has anyone made the transition from app development to ML/AI work?

#56

Post by disgruntledphd2 »

SQueeeeeL wrote: post #44
sroussey wrote: post #33

Earlier quoted context omitted.

This is the #1 thing VC backed startups are trying to automate away

They've been trying to automate away the "grunge" work of building and managing complex software systems since the 80s. Trust me, we'll be fine

To be fair, it's not as funny as automating data cleaning, on the principle that data scientists don't want to do it.

And yeah, lots of people dislike it, but you can't build models without an understanding of the data, so even if automated data cleaning became possible (unlikely) you'd still need to spend a load of time doing work on the dataset before building anything useful.

Re: Ask HN: Has anyone made the transition from app development to ML/AI work?

#57

Post by brutus1213 »

nerdponx wrote: post #36
bilsbie wrote: post #20

Earlier quoted context omitted.

What terms would you search for for those first type of jobs?

Can't speak for FAANG, but you can do well at some companies with "ML Developer" or even "Data Engineer" if you have database admin experience and/or are willing to spend time studying up.

Just wanted to add: DL Engineer != MLOps engineer. DL Engineers who deal with scientists have a much harder job IMHO as the scientists ask for 10 different things, each taking a lot of effort to implement. The current DL stacks (e.g. Tensorflow/keras) make it very easy to implement the standard stuff. But if you want to go off the path, it is really very hard (I wonder if Pytorch makes that significantly easier if anyone cares to chime in).

Re: Ask HN: Has anyone made the transition from app development to ML/AI work?

#58

Post by hermitcrab »

disgruntledphd2 wrote: post #56
SQueeeeeL wrote: post #44

Earlier quoted context omitted.

They've been trying to automate away the "grunge" work of building and managing complex software systems since the 80s. Trust me, we'll be fine

To be fair, it's not as funny as automating data cleaning, on the principle that data scientists don't want to do it. And yeah, lots of people dislike it, but you can't build models without an understanding of the data, so even if automated data cleaning became possible (unlikely) you'd still need to spend a load of time doing work on the dataset before building anything useful.

Some people seem to think they will be able to type "clean my data" into ChatGPT or similar and get a beautiful clean dataset. They are probably descendents of the people who said "COBOL means we don't need programmers any more".

Data cleaning requires a lot of judgement and domain knowledge. Imagine if an AI did clean your dataset. Are you just going to trust it (Hell no!)? Or are you going to spend ages trying to work out what it did, which doesn't seem much of an improvement.

I write data cleaning/ETL software and I'm confident that the need for my product is going to going up between now and when I retire.

Re: Ask HN: Has anyone made the transition from app development to ML/AI work?

#59

Post by nerdponx »

brutus1213 wrote: post #57
nerdponx wrote: post #36

Earlier quoted context omitted.

Can't speak for FAANG, but you can do well at some companies with "ML Developer" or even "Data Engineer" if you have database admin experience and/or are willing to spend time studying up.

Just wanted to add: DL Engineer != MLOps engineer. DL Engineers who deal with scientists have a much harder job IMHO as the scientists ask for 10 different things, each taking a lot of effort to implement. The current DL stacks (e.g. Tensorflow/keras) make it very easy to implement the standard stuff. But if you want to go off the path, it is really very hard (I wonder if Pytorch makes that significantly easier if an…

Good point in general about working with "scientist code". You definitely will want to at least understand some of the math in order to be effective at implementing it.

I don't think Torch is any easier than Keras if you don't know what you're doing.

I think data engineering is probably the easiest transition for software devs with backend web (read: database and Python) experience. Free learning resources abound nowadays, as well as decent-quality books and online courses. It's largely the same set of engineering "macro" skills (proejct planning, cost-time-correctness tradeoffs, levels of abstraction, deploying stuff to the cloud), and the specific engineering "micro" skills (database performance tuning, indexing, storage and compute cost management, setting up ETL pipelines, integrating with dashboard systems like Tableau) should be well within the capabilities of a mid-senior backend web developer.

The hardest part IMO would be getting up to speed to the stats and data analysis basics enough to be conversant with the analysts and data scientists who would be your primary stakeholders and users. But that too can be done with help from the multitude of free learning resources and chatrooms/forums.

IMO it's worth the effort if you're looking to transition. There is a shortage of good, conscientious data engineers right now. Hiring pipelines are clogged with aspiring junior data scientists who are willing to put up with data engineering for a couple of years to try and get some experience on their resume. Data engineers' skillsets are highly portable across companies and industries, and will remain in demand for many years to come, even as companies find that they can't justify hiring a data scientist, so pay and hiring potential will be accordingly high. And as a data engineer you will quickly establish yourself as an essential team member in all but the most dysfunctional organizations, and your value will be immediately apparent whenever someone wants to build a dashboard or pull a report and it all "just works".

The only thing to beware of is the usual caveat of companies trying to short-sightedly hire inexperienced juniors in mid-senior roles to save a buck, setting both you and them up for failure. But that's a caveat in any field where you're starting from the bottom. If you're already an experienced software engineer, you should hopefully be able to either see through that bullshit, or to be able to handle it somewhat more gracefully than a true junior could.

Re: Ask HN: Has anyone made the transition from app development to ML/AI work?

#60

Post by checkcircuits »

safog wrote: post #9
LZ_Khan wrote: post #2

From my experience, there are plenty of teams in FANG that will hire you as a backend developer in a ML team assuming you can pass their interviews. 90% of the work in these teams is not core ML and is more mundane work supporting these models, such as data piping, cleaning, feature generation, experimentation, and real-time serving. You'll get plenty of experience in working directly with ML systems. The jump to cor…

FWIW I worked at Amazon in one of these teams and it's not something I found very interesting. You get thrown a random binary that some ML researcher compiled with instructions to host-it. No sense of ownership over the product, you're just an Ops frontend for a researcher so they can do the fun stuff building models and you're dealing with the pagers.

This has been my experience as a data engineer.

ML guys build the fun stuff, go to conferences, etc. We maintain their stuff and work the 60 hour weeks answering pages.

It's mind numbing. Arguably some of the most boring work I've ever done especially knowing that you're just a CI/CD robot. Nothing has motivated me towards looking into starting a business more than watching other people have fun and you cleaning up their messes.

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