Machine Learning Engineer Interview Experience

Describe your current role, team, project work, tools, AI usage, preferred language, and coding versus leadership split.

I wrote up general background on my current role, the size of my team, the kind of ML project I work on, what tools I use, whether I use AI and which AI tools, what language I'm most comfortable with, and roughly what percentage of my work is coding versus leadership. It felt more like a written recruiter screen than a test.

Simplify a path string and return the valid path.

I treated it like the standard Simplify Path problem. I had practiced a lot of the top Meta-tagged questions already, so this one felt familiar and straightforward.

What stood out was that both interval lists were already sorted, so I didn't treat it like the usual unsorted merge-intervals version. I approached it more like merging two sorted arrays and then collapsing overlaps linearly.

Run the tests, debug the codebase, implement the missing core function, and use the LLM if helpful.

I started by running tests file by file instead of all at once because the console gets noisy fast. The problem was similar to checking word relationships, and I first implemented it assuming alphabet characters only. That passed the alphabet test file, but it broke on the numbers-only file. I had used a bitmask idea, and once numbers were allowed I ran into collisions. I explained that, then adjusted my approach. I also used the LLM for unfamiliar Python syntax, but I had to ignore hallucinated helper functions it invented that weren't in the codebase.

I framed it around defining the recommendation problem, setting metrics, aligning the business objective with the ML objective, then talking through data, high-level features, model choice, and evaluation. The interviewer mostly focused on the algorithm side and on online versus offline evaluation. They moved me along quickly, so in hindsight I would stay high level on feature engineering and not burn time there.