The technical screening comes after the initial phone screen. This interview will involve coding, statistics, and machine learning. You can expect, at least, two coding questions, one involving SQL and the other an algorithm coding type question. The coding portion is done over a shared code editor. Remember to take the time to go over your thought process with the interviewer. There is also a section on “approach”, detailing how you got to the solution and why you use the steps you used.
Interview questions [1]
Question 1
We’re given two tables. Table A has one million records with fields ID and AGE. Table B has 100 records with two fields as well, ID and SALARY.
Looking back, I'm relieved I declined the offer, despite the intense experience. The interview process felt overwhelming, starting with some tough core ML concepts before diving into the LLM fundamentals. During the technical round, I recognized a tokenization question from a PracHub session I had done just a week before. It felt like a small win in an otherwise challenging interview. Ultimately, the pressure and expectations were high, but I felt it wasn't the right fit for me.
Interview questions [2]
Question 1
LLM fundamentals: tokenization design and KL-regularized SFT
There are three rounds in total. The process begins with a coding round, followed by the main interview loop, where you will meet the team and discuss technical skills, experience, and fit.
First round is fun, second round, which is also the final round involved 5 sessions, with different focus. For some sessions, not be able to present my story completely, time was tight, and interviewers were rushing.