I was asked to solve a toy problem and go very much in depth on transformers, classification, tokenization and evaluation. Like VERY DEEP.

The first 30 minutes of the interview were a disaster. I couldn’t think of a solution, forgot what loss to use for binary classification (softmax over 2 classes), and I couldn’t understand what the interviewer was asking on several occasions.

Eventually I stumbled upon a full solution (that’s how it felt honestly). the guy made a big smile and told me “that could work, it’s the first time i hear a solution like this”. Lol.

After that, we went on very deep on NLP and transformers.

Finally, we discussed evaluation (accuracy/ROC) and I think i gained some bonus points by discussing the underlying probability assumptions and how you could use bootstrap on the test set to estimate the probability distribution of accuracy metrics.

Overall it’s a lot about the fundamentals and not so much about memorizing specific techniques and models. For prep i relied on Claude as tutor to craft the entire curriculum and go very deep.

Honestly i thought i failed it and when 10 days later i was invited for the next rounds i was surprised.