What FAANG Interviewers Actually Score You On

Short answer: most big-tech coding rounds grade you on five things: problem solving, code quality, communication, technical depth (complexity and trade-offs), and testing. Each interviewer writes notes against those areas and gives a recommendation. A panel or committee then compares the notes. Finishing the problem helps, but it is one signal among many.

If you search for FAANG interview scoring you mostly find anecdotes. What follows is a synthesis of common practice across large tech companies, not the official policy of any one employer. Exact rubrics and weights vary by company and level, but the areas being judged are very consistent. Use them to review your own recordings, peer mocks, or AI mock interview scorecards.

The real unit: evidence per minute

A coding round is roughly 45 minutes. The interviewer is not just checking whether you finished. They are collecting evidence they can write down:

• Did you drive the problem forward?
• Did you catch your own mistakes?
• Could you explain why your code works?

That is why a candidate who finishes silently can score below one who gets 80% of the way while reasoning clearly.

1. Problem solving

What they watch for

• Restating the problem and its constraints in your own words.
• Choosing a reasonable approach before coding, and revising it when stuck.
• Edge cases: empty input, duplicates, overflow, invalid state.

Strong: you propose two approaches, pick one for a stated reason, and adjust quickly when the interviewer steers.

Weak: random edits hoping the tests pass; ignoring hints.

2. Code quality

What they watch for

• Naming and structure another engineer could maintain.
• Idiomatic use of your language, without needless cleverness.
• Removing dead paths instead of piling up special cases.

Strong: small functions, obvious invariants, readable control flow.

Weak: one giant function, magic numbers, copy-pasted branches.

3. Communication

What they watch for

• Thinking out loud at a useful level of detail.
• Stating intent before details.
• Taking feedback without getting defensive.

This often separates two candidates with the same algorithm. Interviewers are effectively asking whether they would want to debug a production issue with you. How to think out loud covers the habits that score well here.

4. Technical depth

What they watch for

• Correct time and space complexity, with a justification.
• Awareness of alternative structures or algorithms.
• Understanding why a trick works, not just that it passes.

Strong: you name the bottleneck and a realistic way to improve it.

Weak: reciting a complexity you cannot derive from the loops. The time complexity guide helps here.

5. Testing and verification

What they watch for

• Tracing an example before running the code.
• Proposing tests that hit boundaries, not only the happy path.
• Forming a hypothesis when a test fails, instead of thrashing.

How the decision gets made

Each interviewer submits written feedback and an overall recommendation, usually on a scale from strong no hire to strong hire. Then the feedback is reviewed together, in a debrief or by a separate hiring committee depending on the company:

• Outliers get challenged: "Weak on complexity but very clean code. What level are we hiring for?"
• Some companies have a designated reviewer, such as Amazon's Bar Raiser, whose job is to keep the standard consistent.

You do not need a perfect round. You need enough positive evidence across the five areas that the panel can defend the decision to hire you.

Practice that matches the rubric

• Record yourself and score each area from 1 to 5.
• Use mock interviews that show a rubric instead of a single verdict. TechInView's DSA interview scores problem solving, code quality, communication, technical knowledge, and testing separately; how the AI evaluates shows the weights and what each covers.
• After every problem, spend five minutes writing what you would say differently. That habit is what turns feedback into skill.

If you know which area is weakest, the 90-day prep plan shows how to schedule practice around it, and Google vs Amazon vs Meta covers how emphasis shifts by company.

Takeaway

Big-tech companies hire for how you think, not for whether you memorized one optimal solution. Practice against these five areas and you will know why an interview went well or badly instead of guessing.