Interview Resources for Machine Learning, Data Science, AI Research Engineers

ML/Data Science/AI Research Engineer


Purvanshi Mehta, Data Scientist at Microsoft writes a curated list of topics, resources and questions that will help you pass Machine Learning Interviews.

Read our curated list of topics, resources, and questions.

Interviewing is a grueling process, especially during COVID. I recently interviewed with Microsoft (Data Scientist ll), Amazon (Applied AI Scientist), and Apple (Software Development: Machine Learning).

Though all these interviews differed a bit, the basic questions asked were the same. During the process, I curated this list which would help you pass all ML interviews.

NOTE: This list is just for end moment revising

Machine Learning


Deep Learning

The first thing I would suggest to do is to go through all the deeplearnig.ai courses which are pretty basic. If someone already publishes/ works in these topics they might just skip watching all the videos and can go through the following questions/ resources

  • Know what is- K fold cross-validation, dropout, batch norm [Difference between batch norm and layer norm], early stopping
  • Weight decay
  • Calibration (Look to what is calibration and ECE score)
  • Attention (Multi-head, single head) Different Optimizers (Important are- Gradient descent, Adam, RMSprop, Adagrad, Adamax)
  • Initialization


For NLP CS224 covers the basics of NLP with Deep Learning. This might cover 3/4 of the questions asked in an interview. Other questions are usually more state-of-the-art models as the interviewer wants to check how updated you are.


Different types of embeddings (Bag of words, TFIDF, Word2vec(skipgram[How is it trained], pre-trained (Google word2vec, Stanford Glove, fasttext, ELMo))). Need to know how incremental changes were brought into place.

Other topics

  1. Linear Algebra
  2. Probability basics
  3. Stats I had taken a graduate-level Statistics class so I didn’t need to brush this up but Khan Academy is a very good source for learning basics with examples.

These are the topics which are asked in all interviews, obvious then some questions were specific to research I had done. There were also live coding rounds both of algorithms and NN models. Let me know if I missed something.

 Purvanshi Mehta

Thank you to Purvanshi Mehta for letting us publish her blog. You can follow Purvanshi on Medium


Paige Coulthurst, the Operations Director at Soap Media and a Technology category shortlisted candidate in the 2024 Campaign Inspiring Women Awards, offers invaluable insights for...
In this article, Michelle Espinosa from Applause, delves into the need for greater diversity in software development and specifically the training of generative AI to...
Aisha Mendez, Associate Partner for AI & Automation at Infosys Consulting UK, takes a look at why businesses must prioritise AI to stay ahead, unlock...
2023 is passing by in the blink of an eye and all of a sudden we are once again looking ahead to the next year....

This website stores cookies on your computer. These cookies are used to improve your website and provide more personalized services to you, both on this website and through other media. To find out more about the cookies we use, see our Privacy Policy.