Machine Learning Engineer applicants have rated the interview process at Snap with 3.4 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 30% positive. To compare, the company-average is 46.1% positive. This is according to Glassdoor user ratings.
Candidates applying for Machine Learning Engineer roles take an average of 20 days to get hired, when considering 23 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 Machine Learning Engineer according to 23 Glassdoor interviews include:
Phone interview: 41%
One on one interview: 31%
Skills test: 14%
Presentation: 7%
IQ intelligence test: 3%
Background check: 3%
Here are the most commonly searched roles for interview reports -
I applied through college or university. The process took 3 weeks. I interviewed at Snap (Los Angeles, CA) in Nov 2019
Interview
First a phone screen on basic ML questions. Starting from what is a derivative to what a convolution is. Then an onsite. 4 rounds on machine learning and coding questions. The rounds went into depth of machine learning both in design and as well as basics.
Interview questions [1]
Question 1
How to implement np.sum() along a given axis?
What to do if you have a lot of unlabeled samples and little labeled samlpes?
Design a neural network to take a max of two numbers
The interview process for the ML position at Snap was pretty straightforward. It included a mix of machine learning fundamentals and algorithm/LeetCode-style coding questions. Overall, the interviewers were professional and the process was well organized.
Interview questions [1]
Question 1
some basic ML fundamentals question as well as algorithm/LeetCode-style coding questions.
1 phone screen and 4 on site rounds. Round 1 ML theory + leetcode
Round 2 ML discussion latest research papers
Round 3 ML coding
Round 4 and 5 Leetcode
Zoom with HR to verify the details, followed by a technical interview including questions about projects and an applied ML question.
The rest of the process includes three more interviews.
Interview questions [1]
Question 1
Tell me about a project you worked on and theoretical questions related to it.