Software Engineer applicants have rated the interview process at Snap with 3.3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 45% positive. To compare, the company-average is 46.1% positive. This is according to Glassdoor user ratings.
Candidates applying for Software Engineer roles take an average of 19 days to get hired, when considering 156 user submitted interviews for this role. To compare, the hiring process at Snap overall takes an average of 26 days.
Common stages of the interview process at Snap as a Software Engineer according to 156 Glassdoor interviews include:
Phone interview: 41%
One on one interview: 23%
Skills test: 15%
Presentation: 9%
Group panel interview: 4%
Background check: 3%
Personality test: 1%
Drug test: 1%
IQ intelligence test: 1%
Other: 1%
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I applied through a recruiter. The process took 1 day. I interviewed at Snap (Atlanta, GA) in Sep 2016
Interview
40 minutes to complete a programming question in an one on one interview. but the question can be found online. The difficulty level is medium. But it requires bug-free code. It doesn't matter if you have an idea, or know how to code it. The code has to run, and produce the correct output. Proficiency is the key.
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Other Software Engineer Interview Reviews for Snap
One initial screening call. Then 1 coding screen. Then an on-site with 3 coding rounds and 1 system design round. All the rounds were pretty straightforward DSA patterns and SD was also a common question you read about in SD interview prep books.
For the technical rounds, I was asked leetcode style questions. Need to practice Data structures and algorithms in order to do well on the interviews. It's important to explain the code as you go along and clarfiy any questions with the interviewer.
Hard but interesting. Had to go through 1 HM round, 2 coding rounds and 2 systems design rounds. Coding round was hit counter, and message recommendation system. The System design rounds were a bit challenging.
Interview questions [1]
Question 1
Design a recommendation system for messaging to predict the next word while typing