New Grad position, phone technical screen.
You get 15 minutes for introductions and questions, and 45 minutes for coding (on either CoderPad or your own IDE).
All four coding parts build upon each other. I managed to get to the fourth question but ran out of time in the end. I got stuck for about 30 minutes on one part and really struggled with debugging; I think this part killed my chances.
They don't care about algorithmic efficiency as long as you get the correct answer among all test cases. But be good at debugging (seriously, I thought I was fine at debugging, but I really panicked and my mind started blanking out when I couldn't find the bug under such pressure. This is a learning experience for me). Make sure your code is clean and easy to follow.
Parse a string of identifiers and compare it to a list of string identifiers of various countries.
Consider only the first few characters from the identifiers and compare them to the list.
Introduce an additional wildcard character.
Now, your string has values associated with each identifier that denote its priority. Compare this to the list.
Ensure there are no duplicates in your result. Order matters. Consider all edge cases.
The following metrics were computed from 1 interview experience for the Stripe ML Engineer role in United States.
Stripe's interview process for their ML Engineer roles in the United States is extremely selective, failing the vast majority of engineers.
Candidates reported having mixed feelings for Stripe's ML Engineer interview process in United States.