Live Coding Interviews: Best Practices for Interviewers

The Case for Live Coding

Live coding interviews, when done well, are the highest-signal assessment format available for software engineering roles. Unlike take-home assignments or multiple-choice tests, live coding lets you observe how a candidate actually works: how they decompose problems, handle ambiguity, debug issues, and communicate their thinking in real time.

The key phrase is "when done well." A poorly run live coding interview is worse than no interview at all — it creates a stressful, artificial environment that tells you more about a candidate's ability to perform under pressure than their engineering ability. This guide covers how to run live coding sessions that generate real signal while treating candidates with respect.

Choosing the Right Problem

The problem you choose determines the quality of your entire interview. Get this wrong and no amount of good interviewing technique will save the session.

Match the Problem to the Role

This seems obvious but is violated constantly. If you are hiring a frontend engineer, do not ask them to implement a graph traversal algorithm. If you are hiring a backend engineer, do not ask them to center a div. The problem should reflect the actual work the candidate will do.

Good live coding problems for different roles:

• Frontend: Build a small interactive component — a search autocomplete, a filterable list, a form with validation. Focus on DOM manipulation, state management, and user interaction handling.
• Backend: Design and implement a small API endpoint or data processing pipeline. Focus on data modeling, error handling, and system design thinking.
• Full-stack: Build a feature end-to-end, even if simplified. This reveals how someone thinks about the boundary between client and server.
• Infrastructure: Work through a deployment scenario, debug a configuration issue, or design a CI/CD pipeline for a given set of requirements.

Avoid Algorithm Puzzles (Usually)

Unless you are hiring for a role where algorithmic thinking is the primary skill, classic algorithm puzzles are a poor choice for live coding interviews. They test a narrow skill set (competitive programming and memorization of standard algorithms) that has weak correlation with day-to-day engineering work.

If you do include algorithmic questions, choose problems where the algorithm is not the point but a tool. For example, a problem that involves processing a stream of events and maintaining a running summary requires algorithmic thinking but in a practical context.

Calibrate Difficulty and Time

Before using a problem in interviews, have at least two current team members complete it under interview conditions. Track how long it takes them. Then add 50% to that time for your candidate budget.

A well-calibrated problem should be:

• Completable by a strong candidate within the allotted time, including time for discussion
• Approachable by a mid-level candidate who should be able to make meaningful progress even if they do not finish
• Extensible so that candidates who finish quickly can discuss optimization, edge cases, or design improvements

Setting Up the Environment

The tools and environment you provide significantly impact the candidate's ability to demonstrate their skills.

Use a Collaborative Editor

The days of asking candidates to code on a whiteboard (physical or virtual) should be over. Use a collaborative code editor where both the candidate and interviewer can see and edit the code in real time. This enables the interviewer to point out typos, suggest quick syntax fixes, and generally remove friction that is not related to the skills being assessed.

Look for editors that support:

• Real-time collaboration with visible cursors
• Syntax highlighting for common languages
• The ability to run code (even if just a basic REPL)
• A clean, distraction-free interface

Provide a Starter Template

Do not make candidates spend the first ten minutes of a 45-minute interview setting up boilerplate. Provide a starter template that includes necessary imports, a function signature, and any helper code or test infrastructure they will need. This lets them jump straight into the interesting part of the problem.

Allow Access to Documentation

Memorizing API signatures is not an engineering skill. Let candidates reference documentation during the interview. A senior engineer who knows exactly which library to use but needs to check the argument order is demonstrating more practical skill than someone who has memorized a standard library.

Running the Interview

The First Five Minutes Matter

Start with a warm, genuine introduction. Explain the format clearly: how long the session is, what the problem involves (at a high level), whether you expect a working solution or just an approach, and that you are happy to answer questions and provide hints.

These first minutes set the psychological tone for the entire session. A candidate who feels welcomed and informed will perform closer to their actual ability than one who feels tested and judged from the first moment.

Be an Active Collaborator, Not a Silent Observer

The worst live coding interviews involve an interviewer who asks a question and then sits in silence for 40 minutes, watching the candidate struggle. This is not how engineering works in practice, and it does not produce useful signal.

Instead, be an active participant:

• Ask about their approach before they start coding. "How are you thinking about breaking this down?" This reveals problem-solving strategy and gives you a chance to redirect if they are heading toward a dead end.
• Give hints when stuck. If a candidate is stuck on something tangential to the skill you are assessing, help them past it. The goal is to see their engineering ability, not to watch them flounder on a syntax issue.
• Ask follow-up questions. "Why did you choose that data structure?" or "What would happen if the input were much larger?" These questions reveal depth of understanding.
• Simulate a real collaboration. Think of the interview as a pairing session. You are working together on a problem, and you are trying to understand how this person thinks and works.

Adapt to the Candidate

Different candidates work in different ways, and a rigid interview format penalizes those whose style does not match your expectations.

Some candidates think out loud constantly. Others prefer to think silently for a moment before explaining their approach. Some start with pseudocode and then translate it. Others dive straight into implementation. None of these approaches is inherently better — adjust your expectations accordingly.

If a candidate is very quiet, gently prompt them: "Can you walk me through what you are thinking?" But do not penalize silence itself. Some of the best engineers think deeply before they speak.

Handle Mistakes Gracefully

When a candidate makes an error, how you respond matters. Saying "That's wrong" shuts down the conversation. Saying "Interesting — what would happen if we ran this with an empty input?" lets the candidate discover and fix the issue themselves, which is much more informative about their debugging ability.

Evaluating the Session

What to Look For

The code itself is only part of the evaluation. A strong live coding interview assesses:

• Problem decomposition. Did the candidate break the problem into manageable pieces?
• Communication. Could they explain their thinking clearly?
• Code quality. Is the code readable and well-organized, even under time pressure?
• Debugging. When something did not work, how did they diagnose and fix it?
• Collaboration. How did they respond to hints and suggestions?
• Knowledge depth. Did follow-up questions reveal solid understanding or surface-level familiarity?

What Not to Penalize

• Syntax errors. These mean almost nothing in a live setting. Every engineer makes them.
• Not finishing. If the candidate made strong progress and demonstrated good engineering thinking, that is more valuable than a rushed, complete solution.
• Asking questions. Engineers who ask clarifying questions before diving in tend to be better engineers than those who make assumptions.
• Using a different approach than you expected. There are many valid solutions to most problems. Evaluate the candidate's approach on its own merits, not against your preferred solution.

Document Immediately

Write your evaluation immediately after the interview, before your memory fades or is influenced by other interviews. Use a structured rubric that you completed before running any interviews. Include specific examples from the session to support your ratings.

Common Anti-Patterns

Avoid these common mistakes that undermine live coding interviews:

• The gotcha question. A problem with a clever trick that the candidate either sees or doesn't. This tests for exposure to the trick, not engineering ability.
• Moving goalposts. Changing the requirements mid-problem to see how candidates "handle change." This just wastes time and creates confusion.
• The impossible problem. A problem that nobody could reasonably solve in the allotted time. Candidates leave frustrated, and you get no useful signal.
• Evaluating speed over quality. A candidate who writes clean, thoughtful code in 40 minutes is a better hire than one who hacks together a working solution in 20 minutes.
• Testing esoteric knowledge. Unless deep knowledge of a specific framework or tool is genuinely required for the role, do not make it a gating criterion.

Building a Sustainable Interview Practice

Live coding interviews are demanding for interviewers too. Running them well requires preparation, attention, and energy. To maintain quality:

• Rotate interviewers. Do not have the same person run every interview — they will burn out and quality will decline.
• Calibrate regularly. Have interviewers observe each other's sessions periodically and discuss evaluation criteria.
• Update problems. If a problem becomes widely known (candidates discuss interview questions online), retire it and create new ones.
• Gather candidate feedback. Ask candidates about their interview experience, regardless of outcome. Their feedback will highlight problems you cannot see from the interviewer side.

Live coding interviews are an investment of time and effort for both sides. When that investment is respected through thoughtful preparation, fair problems, and humane execution, the result is a hiring process that identifies great engineers reliably while treating every candidate with dignity.