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What FAANG Interviewers Actually Look For

From Engineers Who've Conducted Hundreds of Interviews

March 7, 2026|5 min read|By the Reploop team

I've spent a lot of time talking to engineers who've sat on the other side of the table at Google, Meta, Amazon, and Apple. Not people who passed their interviews — people who conducted them. Who evaluated candidates. Who sat in hiring committee meetings and argued for (or against) offers.

What they told me completely changed how I think about technical interviews.

//Section 01

The Rubric Isn't What You Think

Most candidates assume the interview is scored on: did they get the right answer? Was the solution optimal? Here's what it actually looks like at most FAANG companies:

interview_rubric.json
CategoryWeightWhat They're Evaluating
Problem Solving~30%Can they break down the problem? Do they consider multiple approaches?
Coding~25%Is the code clean, correct, and production-quality?
Communication~25%Do they think out loud? Can I follow their reasoning?
Verification~20%Do they test their code? Handle edge cases?

Key insight: Communication is weighted almost as much as coding. And “problem solving” isn't just “did they solve it” — it's “did they demonstrate a structured approach?”

//Section 02

The 5 Things That Separate Passes from Fails

1

Thinking Out Loud

Every interviewer I talked to said the same thing:

“The best candidates talk me through their approach for 3–5 minutes before writing a single line of code.”

This does two things:

  • It lets the interviewer course-correct you early if you're heading down a wrong path (they want to help you)
  • It fills in the “communication” section of the rubric with strong signal

What most people do instead:

Jump straight into coding, solve in silence for 15 minutes, then explain at the end. This leaves the interviewer with almost nothing to score on communication.

2

Starting With Brute Force

This one is counterintuitive. You'd think starting with the optimal solution shows strength. But interviewers told me the opposite:

“When someone jumps to the optimal solution, I have no idea if they understand the problem or if they memorized the answer. When they start with brute force and then optimize, I can see their thinking process.”

The play: Always acknowledge the brute force approach first. Analyze its time/space complexity. Then explain why you want to optimize and what technique you'll use.

3

Clarifying Questions

The problem statement is intentionally ambiguous. Interviewers want to see you ask:

clarifying_questions.sh
$"What's the input size?"# Tells you what complexity is expected
$"Are there duplicates?"# Changes the approach
$"Is the input sorted?"# Enables binary search
$"What should we return for empty input?"# Shows production thinking

Candidates who dive straight in are missing free points.

4

Time Management

Top candidates have an internal clock. Here's the breakdown:

0–5 min
Clarify the problem, discuss approach
5–10 min
Plan the solution, pseudocode
10–30 min
Code the solution
30–40 min
Test with examples, handle edge cases
40–45 min
Discuss complexity, potential optimizations

Candidates who spend 35 minutes coding and have no time to test are at a huge disadvantage.

5

The Behavioral Round Is Not a Throwaway

At Google, behavioral signals go into your hiring packet and are discussed by the committee. At Amazon, Leadership Principles are the framework for every interview.

Prepare 4–5 stories using the STAR framework (Situation, Task, Action, Result) covering:

01A time you disagreed with a teammate
02A time you shipped under a tight deadline
03A time you made a technical trade-off
04A time you had to learn something quickly
05A time something failed and how you handled it
//Section 03

What This Means for Your Prep

If you're grinding LeetCode problems and nothing else, you're only preparing for ~25% of the evaluation. The engineers I talked to were unanimous: the candidates who fail with strong technical skills almost always fail on communication and problem structuring.

The good news? These are coachable skills. You can improve them in a handful of practice sessions with someone who knows what the rubric looks like.

But even if you never use a coaching platform, I hope this breakdown changes how you prepare. Stop optimizing for problem count. Start optimizing for how you present your thinking.

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If you found this useful, I'd love to hear what surprised you most. And if you've been on the interviewer side — did I miss anything?