Machine Learning Engineer applicants have rated the interview process at ServiceNow with 3.3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 67% positive. To compare, the company-average is 51.7% positive. This is according to Glassdoor user ratings.
Candidates applying for Machine Learning Engineer roles take an average of 30 days to get hired, when considering 3 user submitted interviews for this role. To compare, the hiring process at ServiceNow overall takes an average of 28 days.
Common stages of the interview process at ServiceNow as a Machine Learning Engineer according to 3 Glassdoor interviews include:
Phone interview: 29%
Skills test: 14%
One on one interview: 14%
Group panel interview: 14%
Other: 14%
Presentation: 14%
Here are the most commonly searched roles for interview reports -
I applied online. The process took 4 weeks. I interviewed at ServiceNow (Toronto, ON) in Nov 2023
Interview
Pretty easy algorithm question on Python (String manipulation) followed by extra test cases. After that was a bunch of questions on basic ML algorithms all the way up to LLMs, Transformers, etc.
It was pretty good, had a good experience. It consisted of two rounds. HR screen then panel interview. Standard stuff on ML algorithms and coding. Overall I had a positive experience.
I applied online. The process took 4 weeks. I interviewed at ServiceNow in Aug 2022
Interview
After two phone call and one hiring manager zoom meeting, I entered 5 or 6 (don't remember the exact number) different technical interviews. All interviews were almost the same concept except one which was a design question. Generally, they were ML, NLP and programming questions. Each interview has different programming questions, you can use any programming language to solve. Programming questions were easy to medium/hard data structures/string manipulation/OOP design type of questions. ML/Data Science questions were intermediate-advanced type. But mainly interviews were focused on programming questions, It was like 70% programming and 30% ML/Data Science questions.
Interview questions [2]
Question 1
Explain LSTMs and why they are used? What are the advantages and disadvantages when you compare other architectures. And some follow up questions about it.