Software Engineer - Machine Learning applicants have rated the interview process at Google with 3.4 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 40% positive. To compare, the company-average is 61.5% positive. This is according to Glassdoor user ratings.
Candidates applying for Software Engineer - Machine Learning roles take an average of 49 days to get hired, when considering 10 user submitted interviews for this role. To compare, the hiring process at Google overall takes an average of 38 days.
Common stages of the interview process at Google as a Software Engineer - Machine Learning according to 10 Glassdoor interviews include:
Phone interview: 56%
One on one interview: 33%
Skills test: 11%
Here are the most commonly searched roles for interview reports -
I applied through a recruiter. The process took 4 weeks. I interviewed at Google (Los Angeles, CA) in Oct 2021
Interview
I had a positive experience with everyone I interacted with in the process, but it was such a monumental task to prepare for the interview, I opted out before even doing the technical screen. My reasoning was due to lack of time to properly prepare, and not wanting to go into the tech screen without at least basic preparation. Some say to just do the screen anyway, and even if you're rejected, you can try again in 6 months. However, I really wasn't up to it for a variety of reasons. I let them know several weeks in advance of the tech screen that I needed to cancel, and they were very polite and said to reach out if I ever wanted to resume the interview process. I would do it again, if it was the right time and I had the resources to prepare. It is a serious endeavor, especially for someone like me with no academic comp sci background, but a masters in statistics, which only helps so much in an SE interview like this.
Interview questions [1]
Question 1
Did not make it this far - canceled before interivew. However the study materials sent to me by the recruiter included basic comp sci/SE data structures and algorithms, that I would need to code in a scripting language during the tech screen. The resources were great, very comprehensive, but so much that it would be best to pare down to the essentials. I learned a lot from looking up what other successful candidates had done for their prep. AlgoExpert was my favorite resource, along with books like Cracking the Coding Interview by Gayle Laakmann McDowell and some popular textbooks.
I applied through a recruiter. I interviewed at Google (New York, NY)
Interview
4 rounds (online) - 2 coding, 1 ML systems, 1 behavioral. Coding were standard leetcode style. ML system was expected. Behavioral was also the standard questions. All went pretty smoothly
Screening interview is about a coding problem. It was a 45 mins interview. I had no question about machine Learning. It was only about coding and software engineering. The mistake I did is that I spent too much time explaining my approach on the first question. While the interviewer had more questions for me.
Classical google interview process;
HR phone call,
Algorithm interview and other technical interviews.
Good to have a feedback after every step from hr. However unnecessarily difficult algorithm problems are annoying, after 10 years they are still doing same, no improvement.