At what point do you think there was an inflection point for technical expertise and credentials requires for mid-top tier ML roles? Or was there never one? To be specific, would knowing simple scikit-learn algorithms, or basics of decision trees/SVM qualify you for full-fledged roles only in the past or does it still today? At what point did FAANGs boldly state: preferred (required) to have publications at top-tier venues (ICLR, ICML, CVPR, NIPS, etc) in their job postings?
I use the word 'creep' in the same context 'power creep' is used in battle animes where the scale of power slowly gets to such an irrationally large scale that anything in the past looks extremely weak.
Back in late 2016 I landed my first ML role at a defense firm (lol) but to be fair had just watched a couple ML courses on YouTube, took maybe 2 ML grad courses, and had an incomplete working knowledge of CNNs. Never used Tensorflow, had some experience with Theano not sure if it's exists anymore.
I'm certain that skill set would be insufficient in the 2023 ML industry. But it begs the question is this skill creep making the job market impenetrable for folks who were already working post 2012-2014.
Neural architectures are becoming increasingly complex. You want to develop a multi-modal architecture for an embodied agent? Well you better know a good mix of DL involving RL+CV+NLP. Improving latency on edge devices - how well do you know your ONNX/TensorRT/CUDA kernels, your classes likely didn't even teach you those. Masters is the new bachelors degree, and that's just to give you a fighting chance.
Yeah not sure if it was after the release of AlexNet in 2012, Tensorflow in 2015, Attention /Transformers in 2017 or now ChatGPT - but the skill creep is definitely creating an increasingly fast and growing technical rigor in the field. Close your eyes for 2 years and your models feel prehistoric and your CUDA, Pytorch, Nvidia Driver, NumPy versions need a fat upgrade.
Thoughts yall?
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.
I think that 'flood' is contributing to a market saturation of applicants, thereby making the process more selective.
We also can't ignore the rapid influx of CS students increasingly choosing to study AI/ML versus say general SWE, web development, mobile app engineering like they did a decade ago.
I totally expect there to be a new standardized ML interview process in the same way Leetcode crept up with the tech boom
Sounds like that would be a good thing if “ML leetcode” replaced regular leetcode. The regular leetcode stuff is way harder imo and so pointless to grind
Hey, that's what I am seeing in r/experienceddevs :)
Watch out for that subreddit. The name attracts people who are not experienced but who think highly of themselves. There are a lot of posts in that subreddit that reek of inexperience (especially w.r.t. soft skills) and often a significant lack of self-reflection.
That checks out tbh. I have seen stuff >.>
How? Honesty how is this possible? I literally have senior experience and responsibilities but I can't even get a single callback from anywhere.
I do know what BERT is and how RNNs differ from transformers. What buzzwords should I be putting on my resume to get these interviews?
I’m not good at this myself (or maybe I’m just lazy) but my friend who’s better at this than me told me that your resume should have all of the buzzwords used in the job posting. That’s how you get through the filters etc.
Deep Neural Network, Generative AI, Deep Learning, Large Language Models, GPT, AGI, Universal Function Approximation.
And what should I do if I have no work permit in the US ? 🙃