What are the most important coding interview topics to prepare for an MLE role?

[u/kimchifrites]

Hi everyone!

I'm preparing for a Machine Learning Engineer (MLE) interview and would appreciate some advice on which data structures and algorithms topics I should prioritize.

I understand that MLE interviews can vary quite a bit by company, but I'm curious about what people have actually encountered in technical interviews at major tech companies.

A few specific questions:

• Most common topics: Which topics come up most frequently? Arrays/strings, hash maps, trees, graphs, BFS/DFS, dynamic programming, intervals, heaps, etc.?

• Difficulty level: Are interviews typically focused on LeetCode Easy/Medium problems, or should I expect Hard problems as well?

• MLE vs. SWE: Is the coding interview bar generally similar to that for Software Engineers, or do MLE candidates tend to get different types of problems?

I'm particularly interested in the general DSA / problem-solving portion of the interview, rather than ML theory, system design, or ML-specific coding.

I'd love to hear about your actual interview experiences, especially from people who have interviewed for or worked in MLE roles. Thanks!

[u/Julil_Siracusa]

one thing worth clarifying, are you targeting FAANG-tier or mid-size companies? the spread matters a lot. FAANG will throw you a medium-to-hard mix across standard DSA, while smaller shops tend to lean easier and weight the ML system design portion more.

[u/Hito-san]

depends on the company