The AI-Enabled Coding Round

The Meta AI coding interview, which Meta calls the AI-enabled round, is a 60-minute coding round in CoderPad with an AI chat assistant open beside your editor. You're expected to use it. Not every loop has one. When yours does, it takes the place of one of your two standard coding rounds, and the other stays the classic two-problem format.

The work looks more like a small project than a LeetCode problem. You get a codebase with several files, get its failing tests to pass, build out a missing piece, then make it faster. Interviewers score your judgment about the assistant, so knowing when to overrule it matters more than clever prompting. If your recruiter has confirmed the round, or you're interviewing at E7 or above, this is a round to rehearse on purpose. The sections below cover the setup, the phases, the scoring and a practice routine.

Who gets the Meta AI-enabled coding round?

Many recent loops include it, but not all of them, so don't assume either way. At E6 and below, when it appears, it sits next to one regular coding round, so it replaces one of your two coding rounds. Which of the two gets swapped is chosen at random, so you can't plan around it.

At the most senior levels, roughly E7 and up or M1 managers, the loop can include just one coding round, and that round is the AI-enabled one. Leveling details vary between candidates, so ask your recruiter whether your loop includes it and prepare for both formats until you know.

What the CoderPad environment looks like

The screen has three panels: a file explorer, the code editor and the AI chat. The project is a small multi-file codebase, something like main.py, test.py, solve.py and utils.py plus a data/ folder. You'll spend the first minutes finding your way around it.

Your code runs here. This CoderPad has buttons to run the code and run the tests, so you don't trace by hand the way you do in the classic round. Use that. Run the tests early and often, and let a failing test tell you where to look.

What can the assistant do?

You pick a model from a small menu, and the lineup isn't always the same. Whatever you pick, expect it to be weaker than the ChatGPT or Claude you use at your desk. It reads the files in your workspace and answers questions in the chat. It can't edit your code, so the typing stays yours.

How the round runs, phase by phase

The round usually follows the same arc. Candidates describe three phases, and some describe the build growing over four or five stages, each adding a requirement to what you already have.

How is the Meta AI coding interview scored?

Meta scores responsible use of the assistant. Prompt engineering earns you very little. The areas commonly described are problem solving, code quality, verification and communication. You still own the design and the final code, and you're expected to understand and defend every line, including the ones the model wrote.

Use the assistant for quick lookups and setup work, and solve the core yourself. Stay skeptical where it's weak, such as a subtle bug that only shows up when two tasks run at once, or a confident complexity claim. When it suggests something, ask it to explain why and check that against what you know. Copying its output on faith and finding the bug later costs you. Leaning on it for everything costs you too: interviewers have failed candidates for using the model as a crutch.

The Meta angle

Know when to overrule the AI. Meta wants engineers who use these tools every day, so interviewers test judgment more than prompting. The common mistake is trusting it too much. If you accept a plausible answer that fails on an edge case, you score below a candidate who caught it. Practice with the assistant on, building a small feature, and check every suggestion before you keep it.

What problems does the AI-enabled round use?

Expect one task from a small set of project-style problems. The maze solver and the word-subset task come up most often. The examples below show the shapes people describe. Your task may look different, so use them to practice the format.

• A maze solver that grows in stages. Something like: fix a display bug where the drawn path covers the start and exit, add a visited set so the search stops looping forever, then add one-way gates, and finally keys and locked doors. Once keys exist, the set of keys you hold becomes part of the search state.
• A word-subset task. Pick a subset of words whose letters don't overlap, to get as many distinct characters as you can. It's a backtracking search. The general version has no known fast algorithm, so the optimize stage is about pruning branches and storing each word's letters as a bitmask.
• A card game. You repeatedly pick three cards that sum to fifteen, replace them, and keep going until the deck is dealt. Versions start with a bug about a card that isn't on the table, then ask for a strategy.
• A friend recommender. Validate a social graph, generate friend suggestions and score them by mutual friends, spread across files like recommenders.py and social_graph.py.

How to prepare for the Meta AI coding interview

Start by asking your recruiter for practice material. Candidates say Meta gives out a sample codebase in the same format, and working through it once takes the surprise out of the setup. Then practice with an assistant turned on, ideally a smaller model than the one you use at work, since the interview one is weaker.

Practice the classic format too. At E6 and below, a loop with the AI round still has one standard two-problem round, and both reward reaching a correct, optimal solution fast. The frequency lists in the Meta coding overview are still your best prep for that one.

How it differs from the classic two-problem round

The classic round rewards speed on known problems, and Meta reuses its question pool heavily there. The AI-enabled round rewards reading code you didn't write and checking it under time pressure. Prepare for each separately, since a week of LeetCode drills won't teach you to find your way through a strange codebase.

Common questions