The FAANG Interview Process: What's Shared, What Differs
By Aaron Cao · Updated
FAANG interview processes share a skeleton: recruiter screen, one or two technical screens, then a final loop of four to five rounds mixing coding, system design, and behavioral. The differences are cultural: Amazon grades Leadership Principles with a Bar Raiser, Meta grades explicit signals, Apple hires team by team, and loops run on each company's own platform.
What does every FAANG process have in common?
Strip the company names and the pipelines look almost identical: an application filter, a recruiter conversation, one or two technical screens with live coding in a shared editor, then a final block of four to five interviews, the loop, mixing coding, system design for mid-level and above, and behavioral evaluation. Decisions come from structured debriefs of written feedback rather than any single interviewer's impression, and timelines run weeks rather than days.
That shared skeleton exists because these companies converged on the same problem: hiring at scale with consistent quality. For candidates it is good news. Preparation transfers: narrated coding practice, one solid design narrative per system you have touched, and a bank of behavioral stories serve every pipeline on this page.
Stage-by-stage breakdowns per company live on the company interview processes hub.
Where do the companies actually differ?
If the skeletons match, why does preparation advice fragment by company? Because the grading rubrics differ, and the rubric is what your answers are scored against. This section is the map. Amazon maps behavioral rounds to its Leadership Principles and seats a Bar Raiser, an outside interviewer guarding the hiring bar, in every loop. Meta grades named signals per lane, with famously dense coding rounds, two problems in forty-five minutes is common. Microsoft filters behaviorals through its growth-mindset culture and often closes loops with a senior as-appropriate interviewer. Apple hires team by team, so depth in your specialty and product judgment outweigh any standardized rubric. Google, whose process this library covers in its own pages, leans on structured interviews and committee review.
Logistics differ with ownership too: Amazon interviews run on Amazon Chime, Microsoft on Microsoft Teams, while others use mainstream video platforms with shared coding pads. None of these differences change what you know; they change how you frame it, which is why reading one company page before its loop is worth an evening.
How should you prepare across several FAANG pipelines at once?
Parallel processes are the norm, not the exception, and the trick is separating shared preparation from company framing. Do the shared work once: timed narrated coding, design narratives, and a story bank with real texture in STAR shape. Then apply a framing pass per company, mapping the same stories to Amazon's principles, Meta's signals, or Microsoft's growth-mindset lens the week of each loop.
A full-stack engineer running Amazon and Microsoft loops three weeks apart is a typical case. One story about rescuing a failed launch served both: framed as Ownership and Dive Deep for the Bar Raiser, reframed as feedback-driven growth at Microsoft. Her technical practice never changed; only the vocabulary did. During the live rounds, on Chime and on Teams alike, her local transcript and story bank stayed at glance distance while she spoke.
Rehearse each company's framing out loud with the mock interview tool, and keep the honest limits in view: proctored assessments, recorded rounds, and shared screens are out of scope for assist tools at every company on this page, per the detectability topic.