[D] For those how are involving in hiring and interviewing candidates for ML positions, what are some resources for developing interviews for ML positions ?

Something as basic as, walk me through an NLP deep learning model, soup to nuts, is generally very instructive.

"Build me a simple NLP DL model for NER.", "how do words get turned into numbers that the model could interpret", "Ok, what would you add next", "ok how/should we add attention", "how/should we add normalization", "how should we pick between CNNs or RNNs or something else (like transformer)", "walk me through a Transformer architecture" (if they are more advanced, with a paper reference allowed)

All on whiteboard is generally more than sufficient to wash out a lot of charlatans and/or people who only know how to throw together some Keras layers. And you can get a lot of context on how they approach data science, ML, etc. by asking your "why's" and "what next" and "what would be your top N concerns about what we've built so far".

The above all said...know your domain. If, say, you're doing time series prediction, image analysis, whatever--craft your questions appropriately. Just grab a recent paper or two with a well-specified approach/architecture and build your questions around that (similar to the DL NLP approach above)--tends to work pretty well in my experience. (Of course, you're not looking to *trick* the candidate; but anyone, e.g., who knows DL NLP fairly well should be able to build up intuition to how most transformer components work, for example, even without having looked at the whole thing deeply before.)

The other item that can be helpful: ask them to present a recent ML paper or project to the team. Surprisingly effective in understanding what they prioritize.

/r/MachineLearning Thread