Candidates applying for AI Intern roles take an average of 14 days to get hired, when considering 1 user submitted interviews for this role. To compare, the hiring process at Infobip overall takes an average of 29 days.
Common stages of the interview process at Infobip as a AI Intern according to 1 Glassdoor interviews include:
Skills test: 50%
Group panel interview: 50%
Here are the most commonly searched roles for interview reports -
I applied online. I interviewed at Infobip (Zagreb) in Jun 2023
Interview
3 rounds
1 intro, 1 tehcnical of 2 and a half hours in depth ML algorithms and concepts and follow up interiview with the HR.
The tehnical interview is done by 3 people: 1 from HR and 2 engineers. They go in depth
Interview questions [1]
Question 1
Clustering algorithms, Kmeans, B trees, Red and black trees, Attention mechanism
I applied in-person. The process took 2 weeks. I interviewed at Infobip (Zagreb)
Interview
First there was an online test. After successfully completing the test, they invited me for the interview.
In the email, I was informed that the interview would include some HR questions, a review of the test I had already completed, and an additional programming task.
However, the actual interview turned out to be somewhat different. Instead of just reviewing the test, I was asked a number of new technical questions, some even from topics that were not covered in the initial test. The discussion went deeper than I had expected based on the description.
At the end of the interview, I was given a programming task to solve on the whiteboard, in front of the interviewers.
Interview questions [1]
Question 1
The interview started with the usual introductory questions — I was asked to introduce myself, explain what I was looking for, and what my expectations were.
After that, the technical part began and included a wide range of topics. I was asked many questions related to algorithms and data structures, such as time complexities and the inner workings of various sorting algorithms, as well as questions about concurrency, like what a deadlock is and how to prevent it.
There was also a significant focus on machine learning and deep learning. They asked about logistic regression, accuracy metrics, k-NN, and RNNs, among other related topics.