Meta ML Engineer Interview Guide

Meta ML Engineer interview reports point to a process that tests both software-engineering execution and applied ML judgment. Two candidates described a coding screen before a virtual or onsite loop. One report describes two short LeetCode-style problems in 45 minutes, while another describes an easy and a hard problem in a 40-minute phone screen. Across those accounts, the recurring preparation need is writing correct solutions quickly under pressure, including when there is no terminal to run the code.

For the onsite, candidates reported 4–5 interviews, with coding and ML system design appearing in both detailed accounts. Reported coding topics include trees, graphs, DFS, dynamic programming, sliding windows, string manipulation, and word-ladder-style traversal. The ML-design discussion was described as recommendation, ranking, or feed-freshness design; one candidate was asked to address cold start and evaluation, while another characterized the round as recommendation and ranking focused.

A five-round report also included product sense/metrics and behavioral conversations, including diagnosing a decline in Reels engagement and discussing pushback on a technical decision. Practice explaining tradeoffs aloud, not just naming an algorithm or model. One candidate reported only the post-onsite waiting period, so the available evidence is limited on recruiter follow-up and team matching.