UPDATE: Added to the bottom what questions I got.

I completed the interview process for Facebook for Level 5 (E5) ML engineer, the recruiter just informed me the interviewers' recommendation is that I am a Level 4 (E4) engineer. The hiring process will pause till there's a Level 4 opening by then. He said in 2021 they might make Level 4 openings.

Has anyone faced a similar situation? Don't know if this means I should look forward for an E4 opening in 2021 or forget about it.

I was told though that I was considered on the higher bar of E4.

QUESTIONS that I got:

Remote Interview:
1- You have 2 arrays of flight ticket prices, one array for departure flights and one array for return flights. Each element in the array is the price of a flight ticket on a particular day. The problem is to choose the departure date and return date such that the total cost is minimum.
(to some extent it is similar to: https://leetcode.com/problems/best-time-to-buy-and-sell-stock/ )
2- You have 2 sets of numbers, find all the possible ways to combine the numbers
(similar to this https://leetcode.com/problems/permutations/ )

OnSite Coding Interview 1:
1- (Actually totally forgot what was the question but I recall that I answered it using heap data structure)
2- https://leetcode.com/problems/lowest-common-ancestor-of-a-binary-tree/

OnSite Coding Interview 2:
1- https://leetcode.com/problems/find-median-from-data-stream/
2- https://leetcode.com/problems/add-strings/ (follow up question: what if the numbers were decimal?)

OnSite Behavioural Interview:
1- Tell me what is the best project you have made that you are proud of
2- Tell me about a time you disagreed with your manager or a colleague
3- What do you do when you see that the project is not in the right direction

Onsite System Interview:
Design a system to recommend restaurants that are within X km from you
(Check interviewcamp.io they explained this question in their system design questions

Onsite ML Interview 1:
We have created a new tab in the FB app. When you open the tab, it recommends places for you to explore. Design the ML system for that.
You need to explain what data you will collect, what features, how you will train the model, what is the architecture of the model, how will you evaluate it, how will you deploy it, etc.

(I recommend reading https://developers.google.com/machine-learning/recommendation/ )

Onsite ML Interview 2:
A library hosts weekly events, each event about a certain book. Design an ML system that will help the library predict how many people will attend a particular event.

Similar as above: need to discuss what data you will use, what features, etc.