Meta ML Engineer Interview Guide

Meta ML Engineer interviews reported here put substantial weight on fast, correct algorithmic coding even when the role is ML-focused. One candidate described a 45-minute technical interview with two LeetCode-style problems; another reported an easy and a hard phone-screen problem in roughly 40 minutes. The named problem areas include BSTs, DFS, graphs, DP, sliding windows, string handling, calculator-style parsing, and converting a binary tree to a doubly linked list. Practice explaining a workable solution promptly, then checking edge cases without relying on running code, since one report said a round provided no terminal.

The onsite format is not identical across reports. One candidate described two coding rounds, an ML system-design round, and a behavioral interview. Another described a five-round virtual onsite across two days: ML design, two coding rounds, product sense/metrics, and behavioral. ML design discussion in these reports centered on recommendation or ranking, including feed freshness, cold start for new creators, evaluation, and protecting core ranking metrics. For product sense, one candidate reported diagnosing an 8% drop in Reels engagement and naming the first features or signals to inspect.

Prepare behavioral examples that show how you handled a technical disagreement and what you would change afterward. Evidence on overall pacing is limited; one candidate was still awaiting a post-onsite decision and possible team matching.