They asked coding questions, design questions, machine learning domain questions, statistical questions and behavioral questions. I'd say the questions were very practical and they helped the candidate to understand and think in the real way during the the business
Printing Neatly: Consider the problem of neatly printing a paragraph on a printer. The input text is a sequence of n words of lengths l1, l2, . . . , ln, measured in characters. We want to print this paragraph neatly on a number of lines that hold a maximum of M characters each. Our criterion of “neatness” is as follows. If a given line contains words i through j, where i ≤ j , and we leave exactly one space between words, the number of extra space characters at the end of the line is M-J+i- ∑_(k=i)^j▒l_k , which must be nonnegative so that the words fit on the line. We wish to minimize the sum, over all lines except the last, of the cubes of the numbers of extra space characters at the ends of lines. Give a dynamic-programming algorithm to print a paragraph of n words neatly on a printer. Analyze the running time and space requirements of your algorithm.
Describe experience and job expectations
Where do you see yourself in 5 years?
What is your expecting salary range?
1) Tell me about yourself? 2) Why AMRI? What do you know about AMRI? 3) What is your future goal? 4) How do you handle the pressure? 5) What is the most challenging project? 6) How do you practice writing skills? (talking about publications)
When I gave my job talk, they asked me what my role was on the project I was describing. I explained that I had created the technical design for the study was the lead analyst.
What is your super power/strength? This was the weirdest question I got.
-Round 1: HM technical Review on what I have done so far. -Round 2: Coding : subarrays with length k from an array (easy leet code) ML Design questions: How to detect anomalies from some data? - Onsite Round 1: technical screen with 4 members of the team. Coding: find bboxes given an image (easy) Several ML design questions (some of them mutual between interviewers) with the same theme of detecting anomalies, flare, etc. Deep dive into my experience (every bit of it)
How do you handle ethnics issue?
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