Meta's AI-enabled coding interview: format and how to prepare
By Shilo Drot, software engineer (ex-Meta). Updated .
Meta's AI-enabled coding round is reported to be a 60-minute onsite interview in CoderPad. You work through one multi-file project in stages, with an AI assistant available. Using the AI is reportedly optional. Either way, you have to understand, check and explain the final code.
This page is built from public reports, not from Meta's internal material, and Cla64.ai isn't affiliated with Meta. The format may change and may differ by team, role and level, so treat your recruiter as the final word. For AI rounds at other companies, see the general guide to AI coding interviews.
The reported format at a glance
| Length | 60 minutes (IGotAnOffer, interviewing.io) |
| Place in the loop | Replaces one of the two onsite coding rounds (interviewing.io, DG Learning) |
| The task | One thematic question with several stages, on existing code and tests of around a few hundred lines (IGotAnOffer) |
| The environment | CoderPad with a file explorer, an editor and an AI chat panel (DG Learning, techinterview.org) |
| The AI | A choice of models that you can switch between. It replies in chat and can't edit your files, so you write or paste the code yourself (DG Learning, techinterview.org) |
| Running code | DG Learning describes a Run Tests button that you can use at any time. Check this with your recruiter |
| Who gets it | Reports differ on roles and levels. IGotAnOffer still called it a pilot in its June 2026 update, and interviewing.io dates the pilot to October 2025. Ask your recruiter |
The three reported stages
IGotAnOffer describes the round in three parts. Not every part involves writing code: you may also be asked to analyze runtime behavior, interpret data or justify a design choice.
- Explore and fix issues. Read the existing files, tests and data, then resolve failing tests or small bugs. It also serves as a warm-up on the project.
- Implement new functionality. Complete a clear requirement, such as a new function or feature, by hand or with the AI. You should be able to justify the result either way.
- Extend and improve the system. A broader, open-ended task, such as a more complex feature, a performance improvement or adapting the design to new conditions.
Do you have to use the AI?
Reportedly not. A Meta engineering manager quoted by IGotAnOffer puts it this way: "Using it is optional, and you are not evaluated on how often you use it. What matters is your ability to think critically, understand the code, and make informed decisions."
There's some disagreement about how that plays out. The interviewing.io author, a former Meta engineer, reports the same official line but thinks skilled use still gives you an edge, because it makes you more productive. A practical reading: use it where it saves you real time, skip it where it doesn't, and don't use it just to look like you're using it.
What Meta reportedly evaluates
DG Learning cites a rubric with three areas: problem solving, code development and understanding, and verification and debugging. techinterview.org adds communication as a fourth area, and IGotAnOffer warns that silently pasting AI output can cost you on technical communication. In practice, that means:
- You own the design. You break the problem down and make the architecture choices and trade-offs, whoever typed the code.
- You understand the code. Be ready to say why it works and how it fits the surrounding system.
- You verify. Walk through the logic and run tests instead of assuming that AI output is right.
- You explain as you go. Say why you're asking the AI for something and what you make of the answer.
How to use the AI well in this round
- Understand the problem before you prompt. Restate it, and note the constraints and edge cases first.
- Ask for small, specific things. Good uses include explaining an unfamiliar function, drafting boilerplate, listing edge cases and suggesting likely causes of a failing test. "Solve the whole problem" rarely gives you something you can defend.
- Treat answers as hypotheses. Check them against the code and the tests. A fix that special-cases a test input can turn a test green without solving anything.
- Expect a weaker model than you use at work. The interviewing.io author advises expecting "a rather underpowered model", so read its output closely.
- Make the code fit. Generated code often ignores the project's existing APIs and conventions. Adapt it before you move on.
How to prepare
- Ask your recruiter first. Find out which rounds you'll have, which languages you can use, and whether you can run the code.
- Try Meta's own practice material if you're given it. interviewing.io describes the official practice links as simpler, single-file challenges, so use them to learn the tool and expect the real project to be bigger.
- Practice in code you didn't write. Time how long it takes you to understand a small project and find a bug in it.
- Rehearse with a chat-only AI. Copying code across by hand, reading it first, is a different habit from accepting edits in your usual editor.
- Talk out loud. Practice saying what you asked for and whether you agree with the answer.
- Keep your algorithms sharp. You still need to spot a slow approach, and a traditional coding round is reported to remain in the loop for many candidates.
The interview guide covers these habits in more detail.
Practicing on Cla64.ai: what matches and what doesn't
Cla64.ai doesn't simulate Meta's round, and it doesn't use Meta's questions. An AI mock interview here practices some of the same skills: working in a real project, deciding what to ask an AI, testing your work and explaining your decisions. Here are the differences that matter:
| Meta's round (as reported) | Cla64.ai | |
|---|---|---|
| What the AI writes | Whatever you ask it to, including a full attempt at the task | Only what you spell out. It won't decide the logic, and it asks what you mean if you say "fix it". Some exercises limit it to words only, apart from language and syntax help |
| Getting code into files | You write it or paste it from chat | You write it, or on exercises that allow it, use AI code changes to accept or reject its proposed edits |
| The AI | Several models to choose from | One assistant, with no model choice |
| The interviewer | A person, watching throughout | An AI that answers when asked and asks 2-3 questions about your work when you finish |
| How the work is judged | By your interviewer, against Meta's criteria | Hidden tests or a live demo, depending on the exercise, then a written review with no score |
The biggest gap is the first row. Because our AI won't design the solution, you won't get practice reviewing a large block of code it produced on its own. To rehearse that, ask a general AI tool to attempt a small task in a project you know, then review its answer line by line.
A good place to start is a debugging exercise from the exercise list, since that's closest to the reported first stage. You can also watch a full session first.
Frequently asked questions
What is Meta's AI-enabled coding round?
It's a reported onsite coding round where you work on one multi-file project in CoderPad with an AI assistant available. It reportedly replaces one of the two onsite coding rounds. Meta hasn't published the full format, so the details come from candidate reports and prep guides.
How long is Meta's AI-enabled coding interview?
About 60 minutes, according to IGotAnOffer, interviewing.io and DG Learning. The task is reported to be one thematic question in several stages, rather than two separate algorithm problems.
Do I have to use the AI in Meta's round?
Reportedly not. A Meta engineering manager quoted by IGotAnOffer says using it is optional and you aren't evaluated on how often you use it. An interviewing.io author, a former Meta engineer, thinks using it well still helps in practice because it can make you faster.
Can the AI edit my files in Meta's round?
Reportedly not. DG Learning and techinterview.org both describe an assistant that only replies in chat, so you write or paste the code yourself. Confirm the details with your recruiter, because the setup may change.
Which AI models are available in Meta's round?
Candidates report a choice of models from OpenAI, Anthropic, Google and Meta, which can be switched during the session. The exact list differs between reports and is likely to change. One interviewing.io author advises expecting a fairly underpowered model and reviewing its output closely.
Do I still need to practice algorithms for Meta?
Yes. A traditional coding round is reported to remain in the loop for many candidates, and interviewing.io says data structures and algorithms knowledge is still needed in the AI-enabled round. Ask your recruiter exactly which rounds you'll have.
Does Cla64.ai simulate Meta's round?
No. Cla64.ai isn't affiliated with Meta and doesn't use Meta's questions, CoderPad or a model picker. It practices overlapping skills: working in code you didn't write, using AI with judgment, testing your work and explaining your decisions.
Will Cla64.ai's AI generate code like Meta's assistant?
Only in a narrower way. Meta's assistant reportedly generates whatever you ask for. Cla64.ai's AI writes only what you spell out, won't decide the logic for you, and asks what you mean if you say something vague like "fix it". Some exercises limit it to words only.