Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

Not disagreeing with you (not informed enough to), but top tech company by what measure? Market valuation? Impact of products/services in 2018/2019? Workplace rating?

I'd strongly recommend considering hardware companies in the ML space. They are trying to acquire ML talent but will also value your EE background. This is probably best approached at the usual big ML hardware vendors (NVDA, INTC and AMD) but there are also numerous start ups in the space that may be credible.

Lots of good advice about acquiring skills, I don't have much to add beyond that. I'll just mention that before you jump into the advanced stuff, please understand the terminology and basics very strongly. I've interviewed over twenty people for roles in ML the last year and many (despite having ML on their resume or even some experience in it) could not even explain the difference between training/inference, the meaning of validation, etc. The field is so hot right now that many unqualified folks are trying to get in, often by faking more experience than they really have. In response, I've created a simple 'fizzbuzz' test just so I can quickly screen people.

ML/AI is here to stay, but currently the marketing and potential impact of ML/AI is a bit exaggerated.

This is my issue with the phrase "AI/ML" as a catch all. You have an excellent general skillset, but "AI/ML" encapsulates a wide spectrum of jobs.

ML-SWE: SWE with ML focus - building architecture around models, feature engineering, distributed training, etc. Relatively limited ML knowledge needed (IMO). The math won't be helpful for this role. Much more important to have SWE background. If you want this, keep building your programming knowledge (Python) and read books. Would focus on understanding the popular frameworks PyTorch and TensorFlow b/c your work will likely interface with those.

Research engineer: Mostly for MS/PHD background. Farther away from the product and closer to actual research. This doesn't sound like what you want to do.

Data Scientist: ML is a subset of the knowledge needed. Applied statistics as important, if not more so. Doesn't sound like you want this.

A path forward:

(1) Program a lot. On what? Anything at all, b/c you need programming skill to work as a SWE.

(2) If you want to do ML-SWE, program with an eye towards ML applications. Maybe do a simple cloud project that leverages ML - Google Cloud makes this particularly easy for classification tasks. Focus on breadth here, not depth. No sane person outside of academia can keep up with state-of-the-art and truly understand it. Far too much material, so focus on fundamentals.

(3) Work towards your strengths. You aren't some hotshot kid out of college proclaiming to be an AI guru. That would be silly and no competent recruiter would believe it. You know hardware - and AI (neural networks) leverages a lot of hardware. Why not focus on the hardware side of AI? Demonstrate your knowledge of how/why TensorFlow is so effective across distributed hardware, or how CUDA accelerates NN computation, or why TPU claims vs. Nvidia may be up to interpretation, etc. This should be a natural transition given your background.

TLDR; Know what you really want to do. Your background is valuable. Play to your strengths. Don't ring the bell.

It seems FAANG has transcended its original meaning from being an acronym to becoming a generalized word for "any highly traded growth tech stock"