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 -
Technical rounds including math, computer vision, software engineering and ML quiz. The 3D math problems are hard and hardcore. Asked about GAN and diffusion models. Quiz includes ml basics, feel like being scored against a list of standard answers.
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.