In October 2025, Meta started replacing one of its two onsite coding rounds with an AI-enabled coding interview, where you sit in a CoderPad environment with GPT, Claude, or Gemini built in and are graded on whether you can read, test, and explain the code the model writes. That single change tells you more about where FAANG interviewing is heading in 2026 than any list of LeetCode problems could.

I'll get to the questions. But the questions are the easy part to find. What's harder to find, and what the top-ranking guides keep missing, is how each company actually scores you, and which round is the one that fails most candidates. That's what this is about.

The loop, company by company

Five companies, five different bars. Google and Amazon run the longest loops. Netflix runs the shortest and the most senior. Apple barely standardizes at all, because the team you interview with mostly writes its own rules.

Company	Typical loop	The round that fails people
Google	2 coding, 1 to 2 system design, 1 Googleyness	The second coding round, where they want a second approach
Meta	2 coding (one now AI-enabled), 1 system design, 1 behavioral	The 45-minute two-problem round on pace
Amazon	1 to 2 coding, 1 system design, 2+ Leadership Principles	The Bar Raiser, who can veto the whole loop
Apple	4 to 6 rounds, mostly team-specific, coding plus domain	The team-fit round, which is unscripted
Netflix	Coding, system design, several culture-deck conversations	Culture fit, because the bar is "would we fight to keep you"

Notice the third column. People prep the coding round to death and then lose the offer somewhere else. At Amazon it's the Bar Raiser, an interviewer from outside the hiring team whose entire job is to say no. At Meta it's pace, not difficulty. At Netflix it's whether a director would go to bat to keep you.

Coding rounds, and the thing that changed

Start with the change, because it's the most consequential. Meta's AI-enabled coding round runs about 60 minutes, applies to SWE and EM roles roughly E5 through E7, and at the moment randomly replaces one of your two onsite coding rounds. According to Hello Interview's writeup of the format, Meta scores four things: problem-solving, code quality, verification, and communication. The instruction candidates report hearing is blunt. Use the AI, but understand the code, explain the output, and test it before you trust it.

That's a different skill than racing LeetCode. You're now being graded on whether you can catch a model when it's confidently wrong. If you've never paired with an AI on a real codebase, the first 10 minutes feel strange, and strange is expensive when the clock is the thing being tested.

The traditional rounds haven't gone anywhere though. Here's what actually repeats, by company.

Google

Still the most algorithm-heavy of the five. The trap is the follow-up. You'll solve the first version, and then they push for a better time complexity or a second approach entirely. Solving it once isn't the bar. Solving it, then improving it out loud, is.

Longest substring with at most K distinct characters
Shortest path in a grid with obstacles, then with a twist
Word break, then count the ways, then return them
Minimum insertions to balance a parenthesis string

Amazon

Medium difficulty, heavy on edge cases and testing, and every answer gets tied back to a Leadership Principle. The coding rarely sinks people. The Bar Raiser does. Treat the behavioral thread inside the coding round as part of the grade, because it is.

Number of islands and its variations
LRU cache from scratch
Merge K sorted lists
Kth largest element, plus warehouse and routing flavors

Apple and Netflix

Grouping these because both are less standardized than the other three. Apple questions track the team, so an iOS team asks about memory and concurrency, an ML team asks about evaluation. Netflix hires senior and skews toward production trade-offs over puzzle solving, so expect questions about streaming, ranking, and distributed consistency rather than a clean LeetCode hard.

Apple: implement an LRU cache or a trie, with memory limits
Apple: concurrency scenarios on the platform teams
Netflix: adaptive bitrate or buffer management logic
Netflix: similarity and ranking for recommendations

System design is where senior offers are decided

If you're going for L5 and up, this round carries more weight than the coding rounds combined. The classic prompts still come up. The difference in 2026 is what they probe after you draw the boxes. They want to hear about failure, not just the happy path.

Prompts that still come up

Design a URL shortener with analytics and expiry
Design a chat system (delivery, ordering, presence)
Design a rate limiter
Design a news feed

Newer prompts showing up in 2026

Design a real-time collaborative editor
Design an ML feature store
Design a system for serving an AI model at scale
Design a distributed job scheduler

What they push on after the diagram

What breaks during a regional outage?
What do you monitor, and what alerts page someone?
How do you migrate off the old system without downtime?
What happens at 10x traffic?

A debatable opinion, since people argue about this: I think most candidates over-prepare the breadth of systems and under-prepare the depth on one. An interviewer would rather see you reason carefully about consistency trade-offs in a chat system than name-drop six architectures without committing to any of them.

Behavioral, where each company has a different obsession

Same STAR format everywhere, different thing being measured. Amazon is the most explicit about it, which is honestly a gift, because the rubric is published as the Leadership Principles.

Amazon (map each story to a Leadership Principle)

A time you went past your role for a customer (Customer Obsession)
A time you took ownership of a mess that wasn't yours (Ownership)
A decision you made without the full picture (Bias for Action)
A time you had to earn someone's trust back (Earn Trust)

More on this in the Amazon Leadership Principles guide.

Google, Meta, Netflix

Google: a time you worked through real ambiguity, and how you influenced without authority
Meta: your most impactful project, and a time you shipped fast with incomplete information
Netflix: a time you gave hard feedback, and what "freedom and responsibility" means to you in practice

What we hear from candidates during live rounds

Candidates running the LastRound AI copilot during real FAANG loops keep surfacing the same gap, and it isn't a knowledge gap. They can solve the problem. What they freeze on is the narration. Google asks for a second approach and they go quiet trying to find it in their head instead of thinking out loud. Meta's clock runs and they slow down rather than committing to a working solution first and optimizing second. Amazon's interviewer asks a soft behavioral question mid-code and they treat it like an interruption instead of part of the score.

The other thing we hear, more than anything else this year, is uncertainty about Meta's AI-enabled round. People don't know whether using the model freely is a trap. From what candidates report back, it isn't. The trap is using it without reading the output. The ones who narrate "let me check what it just wrote before I run it" do better than the ones who either refuse to touch the AI or accept whatever it produces.

A realistic prep order

If you have about 5 weeks

Weeks 1 to 2: coding patterns, not problem count. Two pointers, sliding window, BFS and DFS, basic DP. Solve out loud, even alone.
Week 3: one system design per day, drawn and explained, with a focus on the failure questions above.
Week 4: behavioral. Build 7 or 8 stories that flex across principles. Don't write 30.
Week 5: full timed mock loops. If you're interviewing at Meta, do at least one session pairing with an AI assistant so the format isn't new on the day.

Practice the round, not just the problem

LastRound AI coaches you in real time during coding, system design, and behavioral rounds, including the narrate-while-you-code habit that decides most FAANG loops.

One thing worth keeping in view

It's easy to read all this as the bar going up. The hiring side looks different. The BLS Occupational Outlook for software developers projects 15% growth from 2024 to 2034 and roughly 129,200 openings a year on average, much faster than the all-occupations rate. The FAANGs aren't back at their 2021 requisition counts, so the loops are stricter, but the demand for engineers who can actually pass them hasn't gone anywhere. Prep the format, not just the problems, and the format is the part that changed this year.

Sources: Hello Interview, Meta's AI-enabled coding interview (format, scoring, October 2025 rollout), and the BLS Software Developers Occupational Outlook (15% projected growth, 129,200 annual openings, 2024 to 2034). Question patterns reflect what candidates report from recent loops. Live-round observations come from candidates the LastRound AI team has worked with.

LastRound AI

The FAANG Interview Question Patterns That Keep Repeating in 2026