I learned ML only to satisfy my curiosity, so I don't know if it's useful for interviewing. :)

Now when I read a paper on something unrelated to AI (idk, say progesterone supplements), and they mention a random forest, I know what they're talking about. I understand regression, PCA, clustering, etc. When I trained a few transformer models (not pretrained) on my native language texts, I was shocked by how rapidly they learn connotations. I find transformer-based LLMs to be very useful, yes, but not unsettlingly AGI-like, as I did before learning about them. I understand the usual way of building recommender systems, embeddings and things. Image models like Unets, GANs etc were very cool too, and when your own code produces that magical result, you see the power of pretraining + specialization. So yeah, idk what they do in interviews nowadays but I found my education very fruitful. It was how I felt when I first picked up programming.

Re the age of LLMs, it is precisely because LLMs will be ubiquitous I wanted to know how they work. I felt uncomfortable treating them as black boxes that you don't understand technically. Think about the people who don't know simple things about a web browser, like opening dev tools and printing the auth token or something. It's not great to be in that place.