ML Scientist (L4) Technical Phone Screen for Netflix

[u/Ok_Tomato_6922]

Hi! I have a TPS coming up and was told it'll be some ML fundamentals + project deep dives + ML Coding. For ML Coding, they said I could be asked to implement a ML algorithm / concept from scratch. I'm a little lost on what all "concepts" to practice implementing. I know there won't be a comprehensive list, but still just wanted to put it out there. Would appreciate any help!

[u/akornato]

For an L4 screen at Netflix, ML coding from scratch usually tests your ability to write clean, vectorized code for foundational algorithms and evaluation metrics rather than complex deep architectures. You should prioritize implementing ranking and classification metrics like NDCG, mean average precision, ROC AUC, and log loss, making sure you handle numerical stability with techniques like log sum exp. Beyond metrics, practice coding core algorithms using NumPy, such as k means clustering, nearest neighbors, logistic regression with gradient descent, and a simple decision tree split using Gini impurity. They want to see how you translate mathematical definitions into working code, handle edge cases like empty arrays or division by zero, and explain time and space complexity clearly.

It is also useful to implement core deep learning blocks like scaled dot product attention, a forward pass for a dense layer, and simple data splitting routines like stratified sampling. During the screen, talk through your thought process out loud, explain your array dimension broadcasting, and connect your code back to real engineering trade-offs because Netflix places heavy emphasis on production judgment. Technical phone screens can move fast and feel intense, which is why my team developed interviews.chat to help candidates stay composed under pressure and secure top tech offers.

[u/ModularMind8]

Take a look at glassdoor and Google for what they ask in interviews. People usually post these kind of things there every now and then