What Interviewers Are Actually Looking for in a Coding Screen

TL;DR

Correctness matters, but it is not the only thing being scored. Interviewers evaluate communication, decomposition, code quality, and how you handle being stuck.
A candidate who talks through their thinking and catches their own mistakes often scores higher than someone who silently writes a perfect solution.
Problem decomposition before typing is one of the clearest signals of engineering maturity. Most candidates skip it.
How you respond to hints and feedback during the interview is scored as its own dimension.
Your code quality (naming, structure, readability) tells interviewers how you will write code on the job.

Getting the right answer is not enough to pass a coding screen. That surprises a lot of candidates who spent weeks grinding LeetCode problems expecting a binary pass/fail based on correctness.

The reality is that interviewers are running a rubric with multiple dimensions. Some companies make that rubric explicit. Most don't. But the dimensions are consistent enough across companies that you can prepare for them directly.

Here is what interviewers are actually evaluating, and what each signal means to a hiring team.

Signal 1: Communication

The most common feedback interviewers give about candidates who fail is not "they didn't get the answer." It's "they went silent."

When a candidate types quietly for ten minutes and then produces a solution, the interviewer has almost no information about how that person thinks. Did they understand the problem? Did they consider edge cases? Did they know why they made the architectural choices they made? Unknown.

Interviewers want to know what is happening in your head. This is partly because they need to give you a score, and silence gives them nothing to score. But it's also because the job involves communicating constantly: in code review, in standups, in debugging sessions with teammates.

The candidates who perform well in this dimension do a few specific things.

They restate the problem in their own words before touching the keyboard. This confirms they understood it and often surfaces misunderstandings early.

They narrate their thinking as they work. Not a running monologue of every keypress, but something like: "I'm thinking about using a hash map here because I want O(1) lookups. Let me see if that makes sense given the constraints."

They flag uncertainty when they feel it. "I'm not 100% sure about the edge case when the input is empty. I'll handle the main case first and come back to that."

None of this requires you to know everything. It requires you to think out loud. That is a learnable habit, and it is worth practicing explicitly before the interview.

Signal 2: Problem Decomposition

Before you write a single line of code, how do you approach breaking the problem apart?

This is one of the most reliable signals of engineering maturity. Junior candidates often read a problem, feel lost, and start typing hoping the code will figure it out. Experienced engineers pause and ask questions: What are the inputs? What are the outputs? What are the constraints? What are the edge cases? What approach makes sense at a high level before committing to an implementation?

Interviewers notice the difference immediately.

Good problem decomposition in a coding screen looks like this:

Ask a clarifying question or two before diving in. ("Can the input array contain duplicates?" "Should I assume the input is sorted?") You don't need ten questions. One or two targeted ones signal that you think before you code.
Sketch a high-level approach before implementing. ("I think I can solve this with a sliding window. Let me walk through the logic before I code it up.") This takes about two minutes and dramatically increases your accuracy.
Identify edge cases before they bite you. Empty input, null values, very large inputs, duplicate values. Listing them early and noting which ones you'll handle shows systematic thinking.

The reason this matters to a hiring team is simple. Developers who decompose problems before implementing them write fewer bugs, write cleaner first drafts, and are easier to work with in collaborative debugging. The interview is a proxy for that.

For more on structuring your approach when a problem is unfamiliar, see how to approach an unknown coding problem.

Signal 3: Code Quality

Once you do start writing, the code itself is being evaluated beyond "does it produce the right output."

Interviewers read your code the way a teammate would read a pull request. They are asking: Is this code I would understand if I had to maintain it? Is this code that would require a long explanation at review, or does it communicate intent clearly?

The specific things they look at:

Variable and function names. temp, x, arr2 are flags. leftPointer, currentMax, uniqueChars are signals of someone who writes code to be read. This is not pedantic. In a real job, bad naming accumulates into a codebase that becomes hard to work in. Interviewers know this.

Structure and abstraction. Does the candidate put everything in one flat function, or do they recognize when to pull a logical piece into a helper? You do not need to over-engineer a simple problem, but recognizing when a loop body is getting long enough to extract shows good instincts.

Consistency. Inconsistent indentation, switching between styles mid-solution, or leaving commented-out dead code sitting in the solution all signal that code quality is not a habit yet.

Handling error cases and edge cases in the code. You flagged the edge cases in decomposition. Do you actually handle them in the implementation? Candidates who mention an edge case but never address it in the code leave a loose thread that interviewers notice.

None of this requires a perfect solution. A moderately efficient solution with clean, readable code and handled edge cases often scores higher than a technically optimal solution that is impossible to follow.

Signal 4: How You Handle Being Stuck

Every interviewer knows that some problems are hard. Getting stuck is expected. What is being scored is how you behave when you are stuck.

There are two responses interviewers see often, and one of them is damaging.

The first is going silent. The candidate freezes, stares at the screen, types and deletes things, and offers nothing. This is the response to avoid. It signals that you don't have strategies for working through difficulty, and it gives the interviewer nothing to work with.

The second is staying engaged. The candidate names the obstacle: "I'm trying to figure out how to track the state across iterations. I know I need something that persists, but I'm not sure if a stack or a hash map is the right choice here." Then they reason through it out loud, even if slowly.

When interviewers give hints, how you receive them is also being scored. Candidates who take a hint and immediately see its implications ("Oh, if I can assume the array is sorted, I can use binary search and get this down to log n") demonstrate that they understand what they are doing, not just that they can memorize patterns. Candidates who take a hint and still look blank suggest the underlying understanding isn't there.

The practical advice: when you're stuck, narrate the sticking point specifically. Don't say "I'm not sure what to do." Say "I know I need to track which elements I've seen, and I'm deciding between a set and a hash map. The set is simpler but I think I need the values too. Let me try the hash map approach."

That is the kind of stuck that interviewers can work with. It shows you have a mental model. It opens a conversation. And it demonstrates that you will be a useful person to debug with when you're on the job.

For a deeper look at handling moments when you genuinely don't know the answer, see what to do when you don't know the answer in an interview.

Signal 5: Adaptability and Response to Feedback

Some interviewers will ask you to optimize your solution after you've produced a working one. Some will point out a bug. Some will ask "what's the time complexity of this?" after you've finished. These aren't bonus rounds. They are part of the evaluation.

What they're testing is whether you can think beyond your first solution. Can you recognize when your approach has inefficiencies? Can you reason about tradeoffs? Can you take criticism of your code without getting defensive?

The candidates who handle this well treat it as a conversation. "Yeah, I see that my current approach is O(n^2). I think I could get it down to O(n) with a hash map. Want me to walk through that?" is a much better response than staring blankly at the screen.

If an interviewer points out a bug, say thank you and fix it. Don't defend the bug. Don't say "well it works for the main case." The ability to receive feedback gracefully and act on it quickly is a direct proxy for how you will behave in code review.

What the Interviewer's Rubric Usually Looks Like

Most companies score coding screens across a handful of dimensions. The exact names vary, but the categories tend to look like:

Problem-solving approach: Did you break it down methodically? Did you consider alternatives?
Communication: Did you explain your thinking? Were you clear?
Code quality: Is the code readable, consistent, and structured?
Correctness: Does the solution work for the main cases? Edge cases?
Complexity awareness: Do you know the time and space complexity of what you wrote?
Coachability: How did you respond to hints and follow-up questions?

Notice that correctness is one of six dimensions, not the whole score. A candidate who aces communication, decomposition, and coachability but writes a suboptimal solution often passes. A candidate who silently writes a perfect solution but says almost nothing often does not.

Practical Implications for How You Prepare

Understanding the rubric changes how you should prepare.

Grinding more problems is useful up to a point. After that, the marginal return on solving problem 200 vs. problem 150 is small. What makes more progress at that point is practicing talking out loud while you code.

Do mock interviews where someone watches you work. When you practice solo, narrate to yourself as if there's a person in the room. Record yourself once or twice and listen back. Most people are surprised by how much silence there is.

Practice decomposition as a separate step. Before you start coding, force yourself to spend two minutes writing down the inputs, outputs, constraints, and edge cases. This feels slow at first. It will feel natural by the fifth or sixth time.

Review your code before you say "done." Read it top to bottom as if someone else wrote it. Rename variables that are unclear. Add a comment where the logic is non-obvious. This habit takes thirty seconds and sends a strong signal.

The goal is not to perform expertise you don't have. It's to give the interviewer enough signal to evaluate what you actually know. Silence makes that impossible. Talking through your thinking, even when you're uncertain, gives the interviewer material to work with and often reveals more capability than you realize you're demonstrating.

See live coding interview tips for a practical guide to the mechanics of performing well in a real-time coding session.

Coding screens feel high-stakes because they are. But they are not a test of whether you can produce perfect code under pressure. They are a test of whether you think clearly, communicate your thinking, and handle difficulty like someone a team would want to work with.

That is a different thing to prepare for. And it is something you can get genuinely good at with the right kind of practice.

If you want structured support working on your technical interview approach, here's how the Globally Scoped program works.