Machine Learning Engineer applicants have rated the interview process at Meta with 3.4 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 69% positive. To compare, the company-average is 57.2% positive. This is according to Glassdoor user ratings.
Candidates applying for Machine Learning Engineer roles take an average of 49 days to get hired, when considering 13 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 13 Glassdoor interviews include:
Phone interview: 31%
Skills test: 25%
One on one interview: 19%
Personality test: 6%
Group panel interview: 6%
Presentation: 6%
Background check: 6%
Here are the most commonly searched roles for interview reports -
I applied through a recruiter. The process took 1 week. I interviewed at Meta (London, England) in Jan 2025
Interview
Jumping directly into LeetCode style questions - after less than 20 seconds. No introductions, no questions about profile or anything else.
I don't come from a Computer Science educational background - but an engineering one (research w. PhD in engineering), so these questions seem to be targeted to people with Computer Science background independently of the whether you are applying for a Software Engineering position or a Data Science/ML position.
Interviewer friendly. Questions were interesting to think about (as academic exercise or a puzzle - but not sure if very practical for the job), but difficult to solve on the spot if you never practiced LeetCode style questions, you don't have a Computer Science background and if you are not familiar with the process of Meta/FAANG interviews (apparently it is a BIG thing to solve this coding questions -which I didn't know).
Seems like a process that you could hack by learning how to solve these puzzles, but that won't be able to enter if you don't know how to do them, independently on what you are going to do.
Interview questions [1]
Question 1
Questions about 1) Binary Trees and 2) Sparse Matrix computational optimisation.
The interview process went well, went through 2 coding rounds, 1 design and 1 behavior, the coding is two easy questions per round. I think the interviewer expect you to have a good understanding of the algo and want you to talk through it, instead of just implementing it.
Interview questions [1]
Question 1
All the questions are common meta style questions, easy and standard
Applied online. Received a recruiter screen within two weeks covering background and role fit. Followed by a technical phone screen with coding (LeetCode medium-hard, arrays/graphs). Then a virtual onsite with 4 rounds: 2 coding, 1 ML system design (recommendation/ranking system), and 1 behavioral. Interviewers were professional and gave time to ask questions. Results communicated within a week post-onsite.
First stage was a screen round with behavioral and 2 leetcodes, one medium one hard, 15-17 min each. If selected, loop is 4-6 interviews. 2 desgin, 2 coding, 1 behavioral.
Interview questions [1]
Question 1
idiotic questions that can't be answered in depth in 35 minutes design, like "your solution isn't going to work, how will you handle it?" yea no sht this is a baseline bro, wait 15 seconds and ill talk about the optimal one.