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 146 - 150 of 159 Interviews
Meta interviews FAQs
Machine Learning Engineer applicants have rated the interview process at Meta with 5 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 58% positive. This is according to Glassdoor user ratings.
Candidates applying for Machine Learning Engineer roles take an average of 63 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 32 days.
Common stages of the interview process at Meta as a Machine Learning Engineer according to 1 Glassdoor interviews include:
One on one interview: 100%
Here are the most commonly searched roles for interview reports -
There are two 30 min phone interview in same afternoon. The process of two interviews are similar. First, self introduction. Second, two coding questions. Third,asking questions about fb or groups.
I applied through a recruiter. I interviewed at Meta
Interview
45-minute one-on-one interview, two coding tasks (fairly easy), one question about the background, one about recent projects. The feedback was given the same day. Video call is setup via BlueJeans, coding is done via CoderPad.
I applied through a recruiter. I interviewed at Meta in Sep 2018
Interview
Contacted by Facebook recruiter on LinkedIn. The interview was setup one month after. The recruiter called me, asked me how comfortable I was with programming, and sent me preparation material. Though the post was for ML software engineer, the expectation was for all engineers to program algorithms/data structures such as arrays, trees, quick sort, radix sort, heaps, hashes, etc.
Interview questions [1]
Question 1
Q: Give me an example of a project where you used data and machine learning.
Q: Given a binary tree, write a function to find if this tree is a search binary tree or not.
Q: Given an array, write a function that returns a samples from the array.