FAANG Interview Guide 2026: All 5 Compared Side-by-Side
FAANG (Google, Meta, Amazon, Apple, Microsoft) is the default reference point for international students applying to US tech. From the outside they look similar — high comp, structured loops, public rubrics. From the inside, the filters diverge sharply. Same résumé that passes Google can fail Amazon. Same answer that scores at Meta can fall flat at Apple.
This guide compares all five head-to-head, with concrete data per company.
Cultural pillar one-liners
| Company | Core cultural filter | What it actually means |
|---|---|---|
| Googliness | Data-driven, comfort with ambiguity, cross-functional, learning-velocity | |
| Meta | Move Fast | Shipping velocity, end-to-end ownership, experiment-driven decisions |
| Amazon | Leadership Principles (16) | LP signals embedded in every round; Bar Raiser veto power |
| Apple | Craft + secrecy | Team-specific filters; on-device privacy framing; NDA respect |
| Microsoft | Growth mindset + customer obsession | Learning velocity + customer-impact framing; less LP-heavy than Amazon |
Three observations matter most:
Amazon scores LPs continuously. Behavioral signal isn't isolated to one round — every interaction maps to LPs. Resume should pre-tag bullets with LPs in parens. Bar Raiser cross-checks claims with multi-layer follow-up.
Apple is least centralized. Each team runs its own loop with limited cross-team standardization. Same candidate gets different relevance scores at Photos vs Health vs Hardware teams. Customize per team, not per company.
Microsoft has shifted post-2018. The "Microsoft I joined in 2010" framing reads dated. Current Microsoft is Azure-centric, OpenAI-partnered, GitHub-integrated. Reference current-Microsoft work, not legacy enterprise framing.
Interview structure compared
| Meta | Amazon | Apple | Microsoft | ||
|---|---|---|---|---|---|
| Onsite rounds (L4 typical) | 4-5 (1 sys design + 2 coding + 1-2 behavioral) | 4 (2 coding + 1 SD + 1 behavioral) | 4-5 (LP-anchored across all) | Team-specific (varies wildly) | 4 (2 coding + 1 SD + 1 AS-AP manager) |
| Coding difficulty | LC medium-hard | LC medium-hard | LC medium | LC medium (team varies) | LC medium |
| System design | Yes, L4+ | Yes, E4+ | Yes, SDE2+ | Often, ICT3+ | Yes, L61+ |
| Distinctive round | Googliness behavioral | Move Fast metric ownership probe | Bar Raiser (cross-team, veto power) | Team-specific craft round | AS-AP (As-Appropriate) manager round |
| Process length | 4-6 weeks | 3-5 weeks | 6-8 weeks | varies (4-12 weeks team-dependent) | 4-6 weeks |
Compensation comparison (US, May 2026 Levels.fyi)
| Level | Meta | Amazon | Apple | Microsoft | |
|---|---|---|---|---|---|
| L3/E3/SDE1/ICT2/L60 (new grad) | ~$200-230K | ~$210K | ~$205K | ~$195K | ~$190K |
| L4/E4/SDE2/ICT3/L61 | ~$330K | ~$360K | ~$330K | ~$310K | ~$300K |
| L5/E5/SDE3/ICT4/L62 | ~$520K | ~$590K | ~$510K | ~$490K | ~$465K |
| L6/E6/L7 (Staff/Principal) | ~$725K+ | ~$830K+ | ~$700K+ | ~$680K+ | ~$650K+ |
Meta typically leads at senior; Google's signing bonus larger; Amazon caps base + load to bonus and stock; Apple's RSU comp is highest variability (stock-anchored); Microsoft is most stable but lowest absolute. All five offer relocation packages and visa sponsorship.
What each FAANG actually tests in coding rounds
Google — Algorithm depth + systems thinking. Common patterns: graph algorithms (BFS/DFS variants), DP with optimization, sliding window, monotonic stack. Time complexity discussion graded explicitly. Be ready for "what if input scales 100x?" follow-ups.
Meta — Speed + product framing. LeetCode medium-hard but wrapped in product context (News Feed ranking, Messenger delivery). Practical implementation graded over algorithmic novelty.
Amazon — Practical coding + LP signaling. Coding rounds end with LP-anchored behavioral follow-ups. Bar Raiser will probe LP claims you make about past work.
Apple — Team-specific. iOS apps team: Swift fluency. ML team: Core ML + Metal. Hardware: C/C++ at low level. Customize prep per team, not per Apple-the-company.
Microsoft — DSA + customer-impact framing. Coding rounds standard FAANG difficulty. AS-AP round (manager) probes engineering judgment + cross-team collaboration.
Which fits which background
If you're a strong-coding generalist with experiment-driven shipping experience: Meta is most natural. Move Fast culture rewards velocity + metric ownership.
If you're a system-design-first engineer who cares about scale + ambiguity: Google is most natural. Googliness rewards data-driven decisions + cross-functional collaboration.
If you're an operationally-disciplined engineer with strong customer empathy: Amazon is most natural. LPs reward ownership + customer obsession + bias for action.
If you have product taste, design sensibility, and patience for team-specific filters: Apple is most natural. Craft + secrecy rewards depth + NDA respect.
If you have learning-velocity demonstrations + Azure/customer-impact experience: Microsoft is most natural. Growth mindset rewards continuous learning + customer-impact framing.
Common disqualifying mistakes per FAANG
Google: Generic "I want to work in tech" Googliness answers. Reference specific Google products, papers, or open-source contributions you've engaged with.
Meta: Bullets without metric ownership. Every E4+ achievement should have a number tied to product impact. PHP/Hack legacy stack often missed by candidates focused on JavaScript ecosystems.
Amazon: Resume without LP signaling. Tag bullets with LPs in parens — `(Customer Obsession, Ownership)`. Don't just list LPs as a separate section; earn them through documented work.
Apple: Generic iOS dev resume. Each Apple team has different stack signals — customize per Photos / Health / Camera / Wallet / etc.
Microsoft: Outdated framing. Reference current-Microsoft work (Azure, GitHub, OpenAI partnership, Activision) rather than legacy enterprise software.
Practice for each FAANG on Applr
Each FAANG has its own mock interview rubric:
- Google L3 / L4 / L5 SWE | Google PM
- Meta E3 / E4 / E5 SWE
- Amazon SDE1 / SDE2 / SDE3
- Apple ICT2 / ICT3 / ICT4
- Microsoft L60 / L61 / L62
Plus resume polish for each FAANG company (Google, Meta, Amazon, Apple, Microsoft) on /ai-resume-polish.
Bottom line
FAANG is the most-prepared-for cluster of companies in tech. Generic "I prepared for FAANG" framing fails. Each company has filter divergences that matter:
- Google rewards data-driven + scale-aware reasoning
- Meta rewards shipping velocity + metric ownership
- Amazon rewards LP-aligned behavioral storytelling
- Apple rewards craft + team-specific stack depth
- Microsoft rewards growth mindset + customer-impact framing
The candidates who break in to FAANG aren't the ones who prepared "for FAANG" — they're the ones who prepared specifically for the FAANG company they wanted, against that company's actual rubric.
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Practice for each FAANG company on Applr → — first mock free.