Meta AI Research Scientist Interview Guide

Meta AI Research Scientist interviews reported here combine research discussion with practical technical evaluation. Several candidates encountered LeetCode-style coding. Reports describe screens with two easy-to-medium or medium questions, followed in some cases by additional coding interviews containing medium-to-hard problems. Examples included topological sorting and merging sorted arrays. Practice writing working code under time pressure, explaining complexity, and discussing alternative approaches rather than assuming the role will focus only on publications or model theory.

Prepare to explain and defend your research decisions clearly. One candidate was asked how they would choose a research sample for a Meta-platform-related experience and to justify the approach. Other reports mention questions about prior research, technical challenges, PhD difficulties, and a project the candidate was proud of. Build a concise account of your work that covers the problem, your decisions, obstacles, collaboration, and the reasoning behind the final approach.

ML evaluation may focus on applied reasoning. Candidates reported a binary-classification system-design prompt, train/test click-through distribution mismatch, and broader ML-focused discussion. Behavioral conversations covered background, teamwork, ambiguity, and past projects. Reports differ on the exact balance of coding, ML, research, system design, and behavioral interviews, so prepare across those areas without assuming one universal sequence. The clearest recurring signal is that strong research credentials do not replace the need for efficient coding practice.