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.

I solved a common-ancestor style problem on a tree where nodes had parent references. It was one of those questions where the pattern matters more than anything exotic, and it felt in line with the kind of questions I had already seen while prepping.

I used the fact that the array was sorted and looked for the next element strictly greater than k. The round didn't feel harder than the earlier coding screen. To me it was just another standard Meta coding round done by a different person.

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.