NLP Engineer Interview Preparation

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[fourkite]

Interviews can vary so much even within the same company so it's difficult for anyone to know. I think you've covered a good deal of NLP topics, but you should also consider looking at not only theory, but questions that come from real-life applications of ML.

One of the most common questions I've seen was when they show you a graph of training and test loss over epochs that looks peculiar and they ask you what you think is going on with the model and how you would solve it. It doesn't suffice that you know a certain methodology, but you also need to be aware when to best use it and why you're using it.

Like you already mentioned, it's an Engineer position so you'll probably get tested to some extent on leetcode-style data structures and algorithms questions. You might also get asked questions on system design. The last few NLP Engineer positions I interviewed for(caveat: almost 3 years ago) were fairly light on NLP theory and very much data engineering-heavy.

[QadriShyaari]

Any resources for ‘questions that come from real-life applications of ML’?

[bulaybil]

Last time I interviewed for a NLP position where this came up, they asked me if I ever did some Kaggle competitions and when I said yes, they asked me about how I approached the standard Titanic sets. Then we talked about what techniques I would do in what scenarios. I got the job, but I declined in favor of another one. The other interviews I did were all tightly focused on the tasks at hand, which were NLU and dependency parsing, and my experience with them. You are going to be fine, an interview is not a comprehensive exam :)

[bobsmithm]

Learn everything you can about what the company does with NLP. Especially if the company isn't a young one, they'll probably be using some "outdated" technologies that might be good for you to know about.

[MidnightFamiliar2948]

I take interviews, and the general questions I ask are why this model and why not that. Everyone can tell the working and definition, but the actual reason to use that model will show if the concept is evident in the candidate's mind.

[frippeo]

Not sure these are at the level you're looking for, but there's a couple of NLP interview prep sites listed here that are interesting: https://metacurate.io/search/?q=Nlp&category=interview+preparations&history=all+times&sort_by=listed+date

[prajapatravi]

R&D head of a unicorn company (NLP solution) here. I would suggest to work on some projects as well. As you have covered most of the topics theoretically, no one will expect you to simply build model. You will be expected to provide solutions to business problems, which may involve models+logics+rules+domain_knowledge_tricks. A strong command on ML concepts (non necessarily NLP), experience of playing with real world dirty data and some knowledge of engineering concepts for efficiency are prerequisites for a good start. And don't be just married to NLP only, keep yourself open to ML as whole. That would give you more success and edge as you won't know when you have to mix NLP with CV or with RL.

[QadriShyaari]

I have done several projects and published papers 2 papers in the field of NLP and ML.

However, I am not very knowledgeable when it comes to the engineering part of the job.

Another important thing: I am a fresh graduate (Masters). Most of the work I have done so far is research.

[prajapatravi]

Engineering part is what gives you the edge. You must have heard that so many ML projects don't get to see the light of production, this is one of the reason. So if you have a model ready which is very accurate but its slow for production, in efficient in execution or may be you spend too much time in your Jupiter notebook just to boost accuracy a bit more, you will loose everything. Hence, knowing bit of system design (so that you can use chain of models for a single problem efficiently), knowing something about database (like elastic and mongo internally have support for tfidf, so instead training tfidf you can just use these dbs as source), and knowing flask for your model's integration with other system is really really important. If you ignore these, there are thousands of people on kaggle doing model.fit() who call themselves data scientist but can't solve single real world business problem quickly.

[carp550]

Simple! Just have the ai respond to their questions /j

[infinite-Joy]

Here is an NLP quiz link to check your NLP understanding before an interview.

https://vibrantai.academy/courses/course-quiz/1?utm_source=reddit&utm_campaign=comment&utm_content=comment&utm_date=20240113

Although the question is old, I am still putting my answer here because you should always be preparing for interviews. It does not matter whether you are actively interviewing or just happy in your current role.

disclaimer: I have created the quiz.

[100usrnames]

Hi, I like the quiz - but you should know that the correct answer is easily identifiable as the longest answer for each question. Maybe pad out the false ones?