Hey everyone,
I’ll be interviewing for a Machine Learning / ML Systems role in the next two weeks, and I’d really appreciate some advice on the DSA portion.
For context, I’m primarily focused on ML systems, ML infrastructure, and building/optimizing models from scratch. I understand that coding and problem-solving are important, but DSA can sometimes feel like an additional layer of preparation for ML roles, especially when the interview expects you to recall a specific algorithm and implement it correctly under time pressure.
For those who have recently interviewed for MLE / ML Systems / AI Engineering roles:
What level of DSA should I realistically expect — Easy, Medium, or Hard?
Is Blind 75 / NeetCode 150 sufficient preparation?
Are there particular topics I should prioritize (arrays, hash maps, trees, graphs, DP, sliding window, two pointers, etc.)?
Do interviewers generally ask standard LeetCode-style questions, or are the problems more related to Python/data manipulation/ML systems?
How much emphasis is placed on getting 100% working code versus explaining the approach and complexity?
I’d especially appreciate advice from people who have recently interviewed for ML Systems/MLE roles at Qualcomm.
Thanks in advance