I graduated from a very respectable but not elite university, with a respectable but not noteworthy GPA and internship. (I do feel I took to algorithms and data structures somewhat better than most of my classmates, which helped with interviewing years later.)

I worked some jobs in "low engineering" as described here, for several years. I got into this new Android thing, and published some relatively successful apps on the side, before convincing my employer to let me build their Android app, which was also reasonably successful. (I also broke into some light algorithmy stuff to improve ad serving/ad sales for that employer, though it was entirely trivial compared to what "elite" shops were doing.)

I believe that skill was the key to getting contacted by a Google sourcer - there just wasn't much of an external talent pool for it at the time. I failed one interview after insufficient prep, a delayed flight, and very short sleep. They called me again a year later and I prepped some, got in early, and went in fresh.

I imagine ML is maybe the closest thing to an analogous "door-opener skill" over the last few years, but I'm not confident about that. Research roles may really emphasize the sterling academic credentials, and for applied ML roles, Kaggles don't give experience crafting a data set and productionizing a model.