Machine Learning

I really don't think there is a power creep. In 2016, knowing just script-kiddy level ML knowledge like scikit-learn would not qualify you for full-fledged ML roles at FAANGs anyways. In general, FAANGs always looked for 2 qualities in ML roles - rigorous and principled mathematical background, and solid SWE skills.

Architectures are becoming simpler - Transformers are being used instead of LSTMs for sequence modeling. Code is becoming easier to run and streamlined. CUDA is much easier to install these days. Jax is basically streamlined tensorflow 1.

It's the opposite, I'm doing a ton of interviews for senior roles and there's a flood of ex crypto bros and web devs who think they're LLM experts because they used openAI APIs or can use HuggingFace's trainer. Somehow they manage to stuff their resumes with all of the buzzwords and manage to get past recruiters but in interviews can't even give a high level explanation of what BERT is or how RNNs differ from Transformers.

In fairness, offers and compensation packages have also scaled massively, especially for those research-heavy roles you mentioned so the publication requirements are not too unexpected.

I think it never really became a thing. People/Teams/Orgs treated them as SWEs with ML applications and that is more or less what it became. Most seem to just throw xgboost and random models from huggingface at everything and see what sticks.

Also not every domain need deep learning at all. With tabular data gbm is still king (I am happy for every example where DL outperformed gbm on tabular data, as I am also would be happy to use more dl, but I can't just use more complex architectures for the sake of complexity.) Also not every company has infinite data which is needed for the data hungry DL models. So there are situations where knowing transformer architecture would be feasable, but with the available data it is gonna be gbm still. There are still data scientict positions out there where you can still have a big impact on the business as a 'sklearn kiddie' (okay maybe xgboost or lightgbm kiddie) and it would not help any more if you would know all the DL architectures.

In the end it should be all about business impact (if you are working in the competitive sector) and not who is using the latest, freshest architectures.

I think the industry is maturing. When I started back in 2015, it was totally acceptable to simply produce prototypes that were far away from production-level code quality. But the expectation is now to be able to production-grade prediction systems, which means people pay more attention to software development skills. And if you are applying for such a role (which can pay very well), then people will care about that rather than publications.

Yes.

But "the haves" like to pretend its not in order to make it seem like everything's "fair".

Goeff Hinton's 1986 backpropagation research paper is like 4 pages.

Nowaday this is called a brain-fart.

I agree with what most people have said, but it will definitely vary from company to company and even between interviewers.

My personal preference for interviewing candidates has mainly been about someone's thought process around tackling a problem they aren't familiar with. There's such a broad array of people applying for roles that 9 times out of 10 you'll find people can't explain what the feature creation or feature selection process looks like - because in most courses and play datasets these things are done for you.

Many people don't even think about the implications of their answers. You'd be shocked at the number of people who say they would do a grid search to find the optimal parameters on a dataset with millions of rows.

I'd also say in many interviews now the company is setting you up to fail rather than trying to navigate the interview with you to show how you might be useful to the organisation. I would use that to see if the company are a good fit. If a company has 6 interview stages, they don't know what they want. If a company has 1 interview stage they probably aren't being rigorous enough (find out personal fit and technical fit). If a company makes you solve random leet code problems, or explain the architecture of an RNN, when in reality all they do day to day is use scikit-learn, is that interview really fit for purpose?

Wait. I have publications in CVPR and in the ACL Anthology. I also have worked with several variants of BERT and optimized models for both ONNX runtime and TensorRT. I haven’t been looking because I’m happy where I am.

What even is the base skill set people are looking for these days?

I think that as time has passed, the meaning of the DS role has evolved as well. Eventually, stakeholders want a product that works. That hasn't changed. The tools however have changed dramatically. I think that as in other fields of study, where you have to obtain a PhD just to understand the jargon, the DS field experienced the same phenomenon. Yes, it is an industry-biased role, meaning that you don't really have to have a PhD to get hired, but yes, you definitely have to know more than the average DS from a few years ago.

I’m in the same boat as you OP. Got in the back door with a basic:woefully inadequate skill set in 2019, all self taught, somehow into a research role lol. Gunning now for my second role and I think no have a chance but boy is it tough out there even with 4 YOE.

Have you to jump ship from you initial role, and when?

Agree. I got into my 1st ML role last year. All self-taught. I've done all sorts of work - from ETL, CV, sentiment pipeline (mostly SWE stuff) and now LLM-based Information retrieval systems. My work mostly revolves around applied ML but I do have an interest in knowing the bits and bytes of ML as well. So currently teaching myself all about transformers and lms.

But it is also true that, earlier getting started with ML was easy - no need for heavy machinery/resources. But nowadays, you will need high computing power to even get started on learning something about large language models.

Why the lol at the defense firm bit?

Damn this post resonates so hard.

Senior level ML scientist at MSFT here.

Getting the job I think is only slightly more difficult than, say, two years ago. But being a top performer is definitely a lot harder, and it requires a level of SWE skills and pure AI knowledge that the ML industry has never seen.

There's a rift happening between the traditional ML types and scientists that have adapted to work equally well on both sides of the R&D equation. The former are getting left behind and are treated like second class citizens. Not pretty.

I literly have a publication and experience of 2 years and can't get an entry level job for months now.

So I think very.