11–20 of 142 posts
BTW, I think the you may have a bit of an advantage because of the math background you presumably have with a BSEE (linear algerbra & differential equations).
I'm an iOS engineer without a STEM background, and I've been contacted by Amazon recruiters for entry-level ML/AI positions. I thought it was weird, but they said they've hired a few people with iOS backgrounds and no prior ML/AI experience who are now excellent ML engineers. I backed out because I knew I would fail the interview process at this point, but it's something for me to think about for the future.
> what should I do to get noticed by recruiters at FAANG and non-FAANG to stand apart from the CS crowd? The usual answer here is look for suitable business / r&d cases within your own EE industrial domain and use ML/AI as any other tool instead of as a black box or a magic wand. Good luck.
This. I pivoted by finding applications of ML in my day-to-day engineering role, until I got enough internal traction to justify a title change from "____ Engineer" to "Data Scientist." About a year later, I jumped ship to a startup applying ML to my domain, and I am contemplating making the move to FAANG next.
From your question, it sounds like you want to be a software engineer rather than an ML/AI engineer -- is that a fair assessment?
Maybe someone who actually works at FAANG can weigh in, but I would think that one of your best bets would be getting into one as a general SWE and then transitioning to AI/ML internally after a year. I recall Google even having some sort of internal program that encouraged this. Getting into Google is a moonshot, but it's possible to do so with no prior professional programming experience if you put in a ton of effo…
I know ML/AI is all the rage. I just feel that targeting it so heavily is a bit shortsighted.
I've hired about half a dozen ML engineers/architects in the past six months. Several of them have EE backgrounds. There's a good bit of ML that touches on hardware (think integrated cameras and similar) so it can be really helpful. You're mostly on track with your plan to build something. You do need to demonstrate that you have the skill set, but building one giant thing isn't the answer. There's so much that goes…
Patience. Spending that extra time (it helps if you really enjoy it or can program yourself to really enjoy it).
https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_6700... for inspiration.
To answer the question though: I'm not sure what you produce other than maybe blog or publish some analysis using your data skills. And maybe: https://github.com/MaximAbramchuck/awesome-interview-questio...
(source: non-CS engineer at amazon who watched the amazon videos internally before they were made public. I'm not a data scientist yet but sometimes, esp. when people talk about the challenges of AGI, I think about transitioning.)
I can't help but read the title as something akin to "musicians, how did you become comfortable painting with watercolors?" I know ML/AI is all the rage. I just feel that targeting it so heavily is a bit shortsighted.
Maybe that is too simplistic but I can't help after my 1 semester ML course think that most of the ML problems people are solving aren't really suited at all. Like SWE see this cool hammer and now everything is a nail. Maybe I should read up on startups using it successfully for anything but I haven't seen many of those on the frontpage.
Not a full answer, just an observation: most ML people I come across don't have CS backgrounds. Many have backgrounds in physics, math and other STEM fields that have a strong computational component (my own background is in control systems, math modeling and numerical computation). From your question, it sounds like you want to be a software engineer rather than an ML/AI engineer -- is that a fair assessment?