Meta Machine Learning Engineer interview questions
Updated May 31, 2026
based on 159 ratings
Difficulty
Average
Experience
Mostly positive
How others got an interview
62%
Recruiter
Recruiter
22%
Applied online
Applied online
14%
Employee Referral
Employee Referral
1%
Campus Recruiting
Campus Recruiting
1%
In Person
In Person
Interview search
159 interviews
Viewing 86 - 90 of 159 Interviews
Meta interviews FAQs
Machine Learning Engineer applicants have rated the interview process at Meta with 4 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 100% positive. To compare, the company-average is 74.1% positive. This is according to Glassdoor user ratings.
Candidates applying for Machine Learning Engineer roles take an average of 90 days to get hired, when considering 1 user submitted interviews for this role. To compare, the hiring process at Meta overall takes an average of 27 days.
Common stages of the interview process at Meta as a Machine Learning Engineer according to 1 Glassdoor interviews include:
One on one interview: 50%
Skills test: 50%
Here are the most commonly searched roles for interview reports -
Meta’s on-site interviewing includes four to five interviews split into separate rounds. There are three interview categories: Ninja (coding), Pirate (systems or product design), and Jedi (culture fit and behavioral).
Interview questions [1]
Question 1
What is your favorite Meta product, and how would you improve it?
1, Recruiter found me 2. Live coding Interview A Meta employee will explain the coding challenge by oral speaking and need oral conservation with him/her. which is different from other coding challenges (reading text)
Interview questions [1]
Question 1
Why do you choose Meta? DFS challenge, and counter in string
I applied online. The process took 2 months. I interviewed at Meta in Aug 2019
Interview
* 2 leetcode style whiteboard interviews: detecting distance from origin, and interval tree algorithm for time interval calculations * 1 machine learning design discussion: we discussed best features to use and how to be clever when building machine learning models * 1 NLP whiteboarding sessions (picking best model / algorithm to solve a particular information extraction problme)
Interview questions [1]
Question 1
* 2 leetcode style whiteboard interviews: detecting distance from origin, and interval tree algorithm for time interval calculations