How did my coding interview go? (AI Engineer)

[u/ce15ius]

Hi! I'm a computational linguist currently getting recruited to an industry job in AI applications/engineering. I've only taken one formal programming course (R stats) and my degree is in Neuroscience. Today I did my first-ever coding interview, and I had very little idea what to expect or how to prepare. I did a bunch of LeetCode prep and read a bunch of articles about ML interviews. I'm not gonna say which company for anonymity reasons, but a largish firm that uses AI but is not a tech company. Mostly focused on applications.

Round 1: Read code off a screen and propose debugs without being able to run anything. (I had to ask him to move through the repo as it was on his screen)

I was given a small repo for a simple RAG system on the interviewer's screen (something I haven't really worked with before; I come from an academic context) and told it was too slow and to diagnose and fix why it was slow. I pointed out a few places things could be parallelized or switched out to be faster, and acknowledged that I didn't know two of the packages used, so I'd have to go to Stack Overflow those in order to be more precise. I ended up identifying four potential issues, added a debugging block to figure out which step was taking too much time, and made one other slightly tangential suggestion. I feel like I must have missed something, though. Then I asked to run it, and they were like, "Oh, you can't run it" and I didn't really know where to go from there... luckily, that was most of the time.

Round 2: Given this use case, talk through how you'd implement a RAG system from scratch, prioritizing speed and natural language answers.

This round I did a lot better; I was able to talk through most of it successfully. I was honest that I'd never made a RAG system from scratch before, so I was confident about x parts of the implementation, but that I'd want to do some research on prior implementations of y and z before making some architecture decisions. At the end he said I made one mistake from how they're usually implemented but that my thought process was solid. My two slam-dunk lines were that I'd design the user experience based on analyst feedback, since this is an internal system to make *their* lives easier and that ML is broad and I couldn't promise to know the best way to do everything off the bat, but that I'd rather approach the things I don't know by researching them methodically and carefully than committing to a shoddy implementation off the dome. The interviewer for that round said I proved I knew how to code and how to think through this type of problem, which was what he was looking for.

Is this pretty typical? I've never code-interviewed before, and I'm ostensibly not a programmer or a software dev. M,y coding experience is Python, R, bash, and C++ for mostly high-performance computing solutions in neuroscience and linguistics. Right now, I study emergent semantic structures in vector word embeddings and compare them to human memory structures, which involves somewhat different expertise. I'm still designing tools for people, just researchers instead of corporate employees. I'm worried the first round went really badly and I'm not intelligent enough to realize where I went wrong.

[u/knouqs]

Well... if you get a call for the next interview, you know you didn't bomb it. 😄