Meta AI Engineer Interview Questions from Recent Interviews

Artificial intelligence is central to Meta’s long-term strategy, not an adjacent capability. In recent earnings disclosures, Meta reported that AI-driven recommendation improvements increased time spent on Instagram by approximately 24%, directly strengthening engagement and advertising performance. The company has projected $30B–$40B+ in annual capital expenditures, much of it directed toward AI infrastructure and compute expansion. While Meta has not publicly disclosed a specific AI hiring target for 2026, workforce restructuring and the expansion of dedicated AI divisions signal continued investment in advanced AI engineering talent.

This guide is designed to help you navigate that hiring bar with clarity. We break down Meta’s AI Engineer interview process step by step, outline the stages you can expect, and analyze the types of technical evaluations mostly used. You’ll find real AI interview questions reported by candidates, a summary of the core skills Meta prioritizes, from machine learning depth to system design and experimentation, and a focused breakdown of high-impact topics to prioritize in your preparation. Whether you’re strengthening coding fundamentals, refining ML theory, or preparing for large-scale systems discussions, this guide helps you prepare strategically.

Excelling in the Meta AI Engineer interview requires more than academic machine learning knowledge. You’ll be evaluated on coding precision, algorithmic thinking, and your ability to design scalable AI systems that operate across global user bases. Interviewers look for clarity in reasoning, strong experimentation frameworks, and the ability to translate complex modeling decisions into measurable product impact. Below is a structured breakdown of Meta’s AI Engineer interview process to help you prepare strategically for each stage.

As Meta continues advancing generative AI, large-scale recommendation systems, and AI-driven platform integrity efforts, the hiring bar emphasizes engineers who can bridge research and production. Strong candidates demonstrate not only theoretical fluency in machine learning, but also the ability to optimize models for latency, scale distributed training pipelines, and make data-driven trade-offs under ambiguity. To prepare systematically across coding, ML theory, experimentation, and large-scale system design, follow the AI Engineering 50 study plan at Interview Query and build the depth Meta’s interview loops demand.