HR screen, verbal interview, technical interview, then panel interview with questions around Amazon's corporate principles plus a problem solving and another technical. The technical interviews were about manipulating pandas data frames for very basic data subletting or summary statistics. The problem solving interview was more around a data science problem.
Interview questions [1]
Question 1
How would you calculate these statistics (e.g. group wise mean) without a loop?
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.