Meta AI Research Scientist Interview Guide

Meta AI Research Scientist interviews reported here put substantial weight on how well you can explain and defend your own work. Candidates describe research discussions that go beyond a project summary: they were asked to propose an end-to-end research plan with a model, baselines, and evaluation benchmarks; walk through papers or a research statement; and explain implementation decisions such as adaptation methods or multimodal-model design. A research presentation may also become an active technical discussion about methods, alternatives, and tradeoffs.

Prepare your research narrative at implementation depth. Have a clear account of the problem, method, evaluation, limitations, and the choices you rejected. Several reports also include coding, from sparse representations and matrix multiplication to tree DFS, graph traversal, and AI-algorithm implementation. One report included operating-systems and networking fundamentals, including browser URL flow, so the technical scope may not be exclusively ML.

Reported formats vary by team and research area. One candidate explicitly described five interviews—research talk, coding, two design conversations, and behavioral—while others described different combinations of research, coding, and team-focused conversations. For research-area-specific design questions, focus on reasoning from first principles and connecting your answer to the interviewer’s domain rather than relying only on a memorized framework.