Updated by Meta candidates
Machine Learning Engineer Interview Experience
I got feedback that I performed well on the coding interviews even when I did not feel like I performed up to a requisite level, so I think the bar has more flexibility than we may give ourselves credit for.
Interview process
I interviewed for Software Engineer, Machine Learning at the L5 level. My process was a recruiter screen, an online coding assessment, one live coding round, a second recruiter debrief call, and then a final virtual onsite with three coding interviews, one behavioral, and one system design round. The coding rounds were mostly classic algorithm questions, while the system design was a recommendation system for ranking locations in a feed for someone visiting a new city. I actually felt better about the process than I expected because the interviewers seemed more lenient in how they graded me than I would have graded myself.
- Recruiter screen
- Online assessment
- Phone interview
- Final round
Interview tips
I would study more carefully for the system design interview and make sure I am really sharp on the common algorithm patterns, because I made a small mistake on the top-k question by forgetting to use a heap to keep track of the running top k values. Also, I would not assume the bar is as unforgiving as it feels in the moment. I got feedback that I performed well on coding interviews even when I did not feel like I had performed up to the requisite level.
Company culture
My impression was that the interviewers were more flexible in their grading than candidates probably assume, at least on the coding side. The recruiter also made the rubric pretty explicit by talking about clarity, traceability, complexity analysis, and clean presentation, so it did not feel like they were only judging raw correctness. At the same time, the process felt slow on communication. It took a couple of weeks for follow-up after the onsite, I did not get concrete feedback on whether I passed, and even with a competing offer they did not seem to push my application through faster.
Questions asked
Overview
The final virtual onsite was five rounds for me: three coding interviews, one behavioral, and one system design. The coding rounds were pretty standard Meta-style algorithm interviews, and only one of them had a real follow-up constraint. The system design was ML-flavored and centered on ranking locations in a feed. I enjoyed the overall process, but the communication after the onsite was pretty lacking.
Question types asked
Specific questions asked
Given a string containing round, square, and curly parentheses, how many of them are mismatched?
Can you identify the first shared parent without needing to traverse all the way to the root?
Keep track of the k largest elements of a running list.
In a binary tree, keep track of all nodes that exist within the same vertical column.
Output the index of the largest number in a list, and if multiple indices correspond to that value, can you return one at random?
Using string parsing to navigate a file directory, given tokens like '.', '..', and '~', determine the current directory and what operations can be performed on it.
Design a recommendation system for locations in a feed for a user who arrives in a new city and wants recommendations for places to visit.
How would you featurize the data that tells you which locations should rank highly in the feed?
I was given the use case of a user arriving in a new city and wanting recommendations for places to visit. The framing was really about what locations should show up in the feed and how I would featurize the data so the right places rank highly. In hindsight, this is the round I would have studied more carefully for.
Tell me about a time you worked independently.
How do you push development when you do not have the explicit support of the rest of the team?
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