[angarg12]

I have been working as an ML Engineer for a few years now and I am baffled by the bar to entry for these positions in the industry.

Not only I need to perform at the Software Engineer level expected for the position (with your standard leetcode style interviews), but I need to pass extra ML specific (theory and practice) rounds. Meanwhile the vast majority of my work consist of getting systems production ready and hunting bugs.

If I have to jump through so many hoops when changing jobs I'll seriously consider a regular non-ML position.

[dekhn]

there's a bunch of gatekeeping to get into ML. Part of it is that ML people don't want non-ML people to know just how much of what they do is drudgery and how little of it is exciting math, or have competition from people with similar skills. And those roles come with a lot of prestige.

I went through all that and am a SWE again instead of an ML engineer. The one thing I learned from all that? "The very best models are distilled from postdoc tears".

[Jensson]

Getting state of the art performance in ML requires a lot of intuition about equations though. I've seen some of the top ML engineers work at Google, they all have a really good understanding of math, how formulas translates into measurable results etc. An ML education or research background seems less important, if you have that from studying physics or math or anything then it still translates.

I feel the biggest problem for people without an ML background is that you'd think "I don't know what I'm doing, I can't get hired for this job!", but fact is that people with ML backgrounds mostly don't know what they are doing either. They just get standard results by applying standard libraries, any programmer with some math skills could do the same, it is no harder than learning a frontend or backend framework, people just think it would be harder so they lack confidence about it. There are some gotchas you got to learn, but there are a lot of gotchas in both backend and frontend as well.

[xwolfi]

And the same can be said of non-ML IT ! You always contrast better when you understand the whole history behind why you write something a certain way, even if you could just learn on the job seeing it over and over. It's like how they teach proper sorting by giving you all the bad ways first.

Also, it's not often but you do have to show creativity at times, to solve a new problem or something, and having an intuitive theoretical understanding goes a long way vs someone who learned via base mimicry.

I think instead of gatekeeping we could build bridges: be very clear that salary / responsibilities will be lower at first and judge on results. If an ML person is brilliant, he won't be threatened by an idiot Java dev. And if a Java dev is able to produce good results even if the way he reached them is less graceful, then an ML engineer should probably start shifting the second gear :D

[dekhn]

Sure, I've been doing that "intuitions about equations" thing since 1993 (my undergrad thesis was on using gradient descent to train the weights of a dynamic programming algorithm that found e.coli gene). I generally agree, to be a top ML researcher, you need those skills in excess of the average (I worked with quite a few of those people at Google). To do state of the art work? Mostly hard work, lucky guesses, lots of compute power, and a huge support apparatus to make rapid experimentation easier.

But the vast majority of people working in ML don't need that. Sadly, most of the work I did for one of the world's most powerful machine learning systems was literally computing frequencies and then sorting by the frequency, so features that were more common were encoded in smaller varints, saving lots of disk space.

[godelski]

I agree with you, but I also wish more ML people knew more math. Though I think there's a difference between research and production (I'm in research).

[HeavyStorm]

I, on the other hand, am baffled b by the lack of basic engineering skills of mods ML workers at my company. Their code is unmaintainable, and they seem to lack the usual problem solving skills I look for in software engineers.

Then again, my company business model leads to terrible hires anyway.

[bronxbomber92]

This is the same for any specialized software engineering role. Compilers, GPGPU, embedded systems, computer graphics, image processing, etc. In an interview panel for any of these roles, you will be expected to be a competent software engineer and have domain knowledge about the sub-field.

[capdeck]

> ... I'll seriously consider a regular non-ML position.

What about asking for more money at the end? Multi-stage complex interview process eliminates more candidates. Some, like you say, will opt for a developer gig instead, probably because ML wasn't something they were interested in to begin with. That narrows down the list of candidates even more. Either "play the game" and ask for more money or don't play the game at all. Let employers pay extra for polished candidates.

[angarg12]

If what I want is money I think I'm better off getting competing offers as a regular Software Engineer and pumping the numbers.

[ivanamies]

As an "ML Engineer," this is both true and very funny

[emotional_fool]

And most likely will be paid <= software-engineers

[coconut_man]

just like Software Engineer having to pass leetcode round and system design round, I also doubt that ML theory and practice is much harder than system design (theory and practice). Beside that, you get paid more than Software Engineer.

[angarg12]

I can tell you we are not paid more than Software Engineers, but that might be only my company.

One startup asked me this. They gave me a very vague problem statement, and in 2 days I had to find a couple of recent articles relevant to the problem and prepare a presentation explaining my solution and justifying my decisions.