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Meta's AI-Enabled Coding Interview: How to Prepare
By Evan King
Try AI-Coding Yourself
Practice real problems in an AI-enabled environment
Try AI-Coding Yourself
Practice real problems in an AI-enabled environment
In October of 2025 Meta started rolling out a new interview type called AI-enabled coding. An internal Meta message described it as "a new type of coding interview in which candidates have access to an AI assistant. This is more representative of the developer environment that our future employees will work in, and also makes LLM-based cheating less effective."
I've chatted with a bunch of recent candidates to get a firmer grasp on what the new AI-coding interview actually looks like in practice. I'll do my best to keep this blog up to date as we learn more.
What Is It?
You get 60 minutes in a CoderPad environment with a file explorer on the left, code editor in the middle, and an AI assistant panel on the right. The AI chat window is similar to Cursor or Github Copilot in that it has context of the files in the project, but it can only respond in the chat panel. It can't directly edit your files. You still need to write or paste all code yourself. The system offers multiple models you can switch between during the interview, including the SOTA:
- GPT-4o mini
- GPT-5
- Claude Haiku 3.5
- Claude Haiku 4.5
- Claude Sonnet 4
- Claude Sonnet 4.5
- Claude Opus 4
- Gemini 2.5 Pro
- Llama 4 Maverick
Pick the most capable model available and stick with it. Candidates report that some models work better for different tasks, but switching mid-interview costs you time. GPT-5 was mentioned as being too slow for interview conditions, while the Sonnet models are more capable but may also have longer response times.
Meta CoderPad AI Models
While Meta now offers more capable models like Claude Sonnet 4.5 and Gemini 2.5 Pro, the AI will still make mistakes or suggest suboptimal solutions. Meta wants to see that you can catch these issues and course-correct, not just accept whatever the model generates. Think of this as a pairing session where you're the senior engineer reviewing your junior partner's code.
Instead of two unrelated algorithm puzzles like in the traditional Meta coding interview, you work through one extended problem with multiple stages, typically three phases that progressively increase in difficulty. You'll receive a mini-project or multi-file codebase with source files, dependencies, and data files or input dictionaries. You are expected to read it, modify it, extend it, and make it work.
The supported languages are Java, C++, C#, Python, Kotlin, and TypeScript. You can choose your preferred language from this list—confirm with your recruiter if you have a preference.
Not completing all phases is perfectly acceptable. One candidate only finished two of three parts and still passed. What matters is how you approach each phase and whether you can articulate what you'd do next.
A guaranteed way to fail this interview would be to prompt your way to success, never writing any code yourself or reviewing the AI's output. Don't do this. Meta wants to see that you're still in charge, using the AI as an accelerator, not a replacement.
Interview Flow & Timing
The first 5-6 minutes are spent with the interviewer orienting you to the platform, even if you've already used Meta's prep environment. After that, the interview follows a three-phase structure:
Phase 1: Bug Fixing (No AI in Some Cases)
The interviewer will point you to the codebase and a set of failing test cases. Your job is to find and fix the bug.
Some interviewers now explicitly require you to work without AI during this phase. The policy varies by interviewer. Some allow AI freely, others want to see you read code, identify the algorithm, and reason through the bug on your own. One candidate was asked to explain what the code does, what the unit tests are testing, identify the algorithm type, and give time/space complexity, all before touching anything.
Bugs range from simple syntax errors to conceptual issues like a missing visited set in BFS. The interviewer will ask you to explain what was wrong and how you fixed it.
If you spot something beyond the bug, like suboptimal space complexity or an unnecessary data structure, call it out proactively. Candidates who demonstrated this kind of awareness got strong positive signals from interviewers.
Phase 2: Core Implementation (AI Allowed)
Now you implement the main algorithm or feature. This phase is significantly harder. Candidates describe the problems as harder than medium LeetCode, with substantial code volume (one estimated roughly 120 lines needed). These problems are designed with AI assistance in mind, so the bar for what you're expected to produce is higher than a traditional coding interview.
AI usage is essentially required here given the time constraints. Use it for boilerplate, implementing known data structures (tries, heaps, etc.), and generating helper functions. But the core optimization insight? That needs to come from you.
Phase 3: Optimization
Test cases are tiered with progressively larger or different input sets, and your initial solution will probably time out on the harder ones.
Meta often uses multiple data dictionaries or input files that stress different dimensions. For example, one dictionary might have thousands of short words while another has fewer but much longer words. Your solution might need completely different algorithmic approaches depending on the input characteristics. A trie is faster for many short words, but a greedy approach might win for fewer long words.
Optimization here isn't always about changing your big-O complexity. Sometimes it's about filtering duplicates, pruning the search space, or recognizing that different data distributions call for different strategies. One candidate implemented a trie optimization, benchmarked it, then explained to the interviewer that a greedy approach would actually be faster for the second dictionary due to its input characteristics. That kind of nuanced analysis scores well.
After orientation, bug fixing, and initial exploration, your effective working time for the main implementation and optimization is roughly 30 minutes. Time management is critical. Don't spend 15 minutes perfecting phase 1 when phases 2 and 3 carry more weight.
Throughout the interview, the interviewer may challenge your approach by asking "what about this edge case?" or "have you considered this alternative?" This isn't them saying you're wrong. They're testing whether you understand your solution deeply enough to defend it or adapt it. Treat it as a collaborative discussion, not an interrogation.
Known Problems
Rumors suggest the total pool is around 9 problems, with only about 4 showing up regularly. The problems closely resemble the practice questions Meta provides. Here is an up to date list of what the community is reporting being asked:
Live, up-to-date
Most commonly asked AI-Enabled Coding questions
Meta
The Program Output panel doesn't clear automatically between runs, and if you scroll up, it won't auto-scroll back down on new output. You need to manually clear it or double-check that you're looking at the newest logs. These small friction points can cost you time if you're not aware of them.
Who Gets This Interview?
This is being used for Software Engineer (SWE) and Engineering Manager roles as part of the onsite loop, all the way up to E7 and M2. You historically would have had two coding rounds, one design round (for mid-level or higher), and one behavioral. Now, one of those coding rounds is the AI-assisted format.
You'll still have a traditional coding interview. One classic LeetCode-style algorithm problem interview (no AI) and one AI-enabled interview. Your recruiter will tell you which is which ahead of time.
What Are You Being Evaluated On?
Meta says they're looking at four things, notably, these are the same four competencies that are used to evaluate candidates in the traditional coding interviews as well:
Problem Solving – Do you demonstrate the ability to understand, analyze, and break down complex problems into manageable parts? Do you use logical reasoning and critical thinking to arrive at effective solutions?
Code Quality – Do you write clean, maintainable, and efficient code? Do you follow best practices and coding standards? Do you demonstrate a deep understanding of algorithms, data structures, and design patterns?
Verification – Do you ensure the functionality and reliability of code by writing comprehensive tests? Do you understand the importance of different testing methodologies (unit, integration, system) and apply them appropriately?
Communication – Do you clearly articulate thought processes, design decisions, and code implementation? Do you collaborate effectively with peers, seeking and providing feedback when necessary?
You're not being evaluated on prompt engineering. Meta explicitly says they're testing classic coding skills with AI as a tool that makes the interview more realistic. The interview is designed to feel like a pairing session where you have unlimited access to AI assistance, but you're still the lead engineer making the decisions.
Using AI is encouraged and expected. But you need to arrive at your own solution approach before reaching for the AI panel. When you do use it, you need to demonstrate that you actually understand what it gives you. If you paste a block of AI-generated code into your solution, expect the interviewer to ask you about it. What does this function do? Why did you choose this approach? What's the time complexity? If you can't answer those questions confidently, you're better off writing it yourself.
You also need to test AI-generated code before incorporating it. Run it, check the output, make sure it does what you think it does. And you absolutely cannot prompt your way out of a problem you don't understand. Interviewers can tell the difference between someone who is using AI to accelerate their own thinking and someone who is using AI to replace it.
In practice, what matters more is whether you can read and make sense of a large codebase quickly. Can you figure out where you need AI help versus what you can reason through yourself? Do you understand the code before you start using AI, or are you just throwing prompts at it and hoping for the best?
The candidates who did well consistently showed that they understood the algorithm, the complexity, and the test structure before they ever touched the AI panel.
Candidates report mixed results with AI helpfulness. The more capable models (Claude Sonnet 4.5, Gemini 2.5 Pro) can provide solid scaffolding and catch bugs, but they still give suboptimal suggestions. One candidate said "writing code myself was faster for most tasks" and primarily used AI for syntax questions. Some candidates have ditched the AI entirely and reported it as their strongest round. One recruiter explicitly warned that "you can be marked down if you use it as a crutch." If you're fast at coding, just code.
How to Use the AI Effectively
The AI is useful for writing boilerplate, implementing well-known data structures (tries, heaps, graphs), computing time complexity, generating helper functions, and benchmarking different approaches. Where it falls short is finding the core optimization insight, solving problems end-to-end, or reliably identifying bugs.
Write information-rich prompts. Give the AI full context so you get a good answer on the first try instead of going back and forth. Don't say "fix this." Say "implement a trie class that supports insert and prefix search, where each node stores a character and a boolean for end-of-word."
Tell the AI your intended approach and ask it to compute the time complexity before you start implementing. This validates your plan quickly and shows the interviewer you understand the tradeoffs. If the complexity looks wrong, you can course-correct before writing a single line of code.
You can also use the AI to rapidly benchmark different approaches. Have it generate two implementations and compare them against different input profiles. This is exactly the kind of AI-assisted experimentation that plays well in this format.
There are persistent rumors among candidates that the AI in the interview environment is nerfed. Specifically, that the models are given system-level instructions to avoid pointing out bugs or giving away solutions directly. Multiple candidates have reported the AI giving surprisingly unhelpful responses during the interview, even with capable models. Whether this is true or just inconsistent model behavior, the practical takeaway is the same: do not count on the AI to bail you out. You need to be able to identify bugs and find optimization insights yourself.
What NOT to Do
Never copy-paste AI output without understanding it. Always verbally explain what the code does and why you're using it. If you don't understand an AI response, say so. It's genuinely better to write it yourself than get caught unable to explain pasted code.
Don't skip verification either. Quickly pasting AI responses without checking is a red flag for interviewers. And don't ask the AI to "solve this problem for me." Guide it with specific, targeted questions instead. Interviewers are specifically watching for candidates who try to prompt their way to the solution.
How to Prepare
Practice in the CoderPad Environment
Recruiters provide candidates with a practice environment before the interview. If they didn't send it proactively, ask your Meta recruiter for the practice link. This access is only available to candidates with scheduled interviews.
Meta provides a practice puzzle in the same CoderPad environment. Candidates consistently say the practice puzzle is actually harder than the real interview problems. Getting the solution to run under the benchmark threshold is extremely difficult. It's essentially a known hard optimization problem. The AI in the practice environment also appears to behave the same way as in the real interview, so it won't hand you optimal solutions. The puzzle is still hugely valuable for getting familiar with the UI, the workflow, and the feel of prompting under constraints. If you can handle the practice puzzle, the real interview will feel more manageable.
Meta CoderPad Practice Environment
The practice environment helps you get comfortable with the AI chat panel and model switching, running tests and reading their output, navigating the folder structure, and working around quirks like the output panel not auto-clearing.
Practice Problems
The mock questions Meta provides are very similar to (or the same as) the actual interview problems. Study all of them thoroughly.
Beyond Meta's own materials, LeetCode's design section is the closest match I've found. These are practical coding problems where you build multi-class systems with state management, edge cases, and multiple methods working together.
Still practice LeetCode hards on your own, without AI. You need to be able to solve hard problems independently since the AI may not reliably help with the core algorithmic logic.
Work through problems with an AI assistant in a separate window, practicing both of the following scenarios:
- Building from scratch: Take a design problem and implement it as a complete module. Use the AI to help with boilerplate and scaffolding, but write the core logic yourself. Focus on class design, method signatures, and how the pieces fit together.
- Extending code: After solving a design problem, come back a day later and extend it. Add a new feature, modify the API, or change the data structure. This simulates reading unfamiliar code and figuring out where your changes fit.
Focus your algorithm prep on backtracking, BFS/DFS, greedy algorithms, tries, and DP fundamentals. These are the algorithm families that keep showing up in candidate reports.
Get obsessive about edge cases. The bar is higher in this format because you have AI help with the boilerplate. Start by writing a requirements checklist or talking through what needs to be handled (empty inputs, null values, large N). Write a few unit tests first if it makes sense. This ensures you don't forget about edge cases and gives immediate feedback.
Also, make sure you spend time reviewing AI-generated code line by line. Code review is becoming more and more important in the age of AI, even outside the context of this interview.
Debugging in the CoderPad Environment
For debugging, you can print outputs as usual, but the environment also supports Python's interactive debugger:
- Python: Use ipdb for interactive debugging
- General: Print statements work but remember the output panel quirks mentioned earlier
Don't spend excessive time debugging AI output. Multiple candidates reported that reviewing AI suggestions sometimes takes as long as just writing the code yourself. Use AI strategically for scaffolding and boilerplate, but don't hesitate to write code directly when it's faster.
Your Mindset Going In
Treat the AI as an assistant, not the driver. You should be leading the solution at all times.
Having basic algorithm knowledge is essential here. Not because you'll be asked to recite textbook definitions, but because it lets you form hypotheses before you even read the code. When you see a BFS that's looping infinitely, your gut should say "visited set issue" before you've even traced through the logic. When you see a substring problem, "trie" should already be on your radar.
If you already know the solution approach, explain the full optimal strategy upfront even if you can't implement it all in time. This shows the interviewer your thinking goes beyond what you can physically type.
Practice reading large codebases under time pressure. That's really the core skill being tested. Not whether you can solve a LeetCode problem, but whether you can navigate a multi-file project, understand the relationships between components, and figure out where your changes need to go.
Over-communicate with your interviewer throughout. What you're thinking, what you're doing, why you're copying something from the AI. The interviewer is mostly observing, but they need to hear your thought process to evaluate you.
Pay close attention to the data and input files in the codebase. The optimization catch is often hidden in the characteristics of the test data, not in the algorithm itself. Understanding what the inputs look like can tell you which optimization strategy will actually work.
If you're already comfortable using AI while coding at work, this interview will feel natural. If you're not, spend time before the interview getting used to prompting models for coding tasks so you can write effective prompts under pressure.
Other Companies Doing AI-Enabled Coding
Meta isn't alone. Other companies are experimenting with similar formats, though the implementations vary quite a bit:
LinkedIn has confirmed AI-enabled coding rounds where candidates are instructed to use AI for code examples, test cases, and edge cases. The expectation is that you come up with the solution yourself rather than feeding the entire problem to the AI. You're expected to use AI for "the obvious and tedious parts" like generating test cases.
Shopify and Rippling take a different approach. Candidates use their own Cursor environment with a README describing the challenge. These tend to be more open-ended design challenges rather than algorithmic problems, which changes the dynamic of how you interact with AI during the interview.
DoorDash is testing a variant where candidates share their entire screen with an IDE like Cursor and interact directly with AI agents. This gives access to more capable models (like Sonnet 4.5 or Opus) compared to Meta's setup.
The format is evolving fast across the industry, so expect continued changes in the coming months.
Help Us Keep This Updated
This interview format is still evolving. If you've gone through Meta's AI-enabled coding interview, drop a comment below. What was the problem structure like? Which AI model worked best for you? What surprised you?
The more we share, the better prepared everyone can be. If you spot anything outdated or inaccurate here, call it out so I can fix it.
Good luck!
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About The Author
Evan, Co-founder of Hello Interview and former Staff engineer at Meta, possesses a unique vantage point having been on both sides of the tech hiring process. With a track record of conducting hundreds of interviews and securing offers from top tech companies himself, he is now on a mission to help others do the same.
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On This Page
What Is It?
Interview Flow & Timing
Phase 1: Bug Fixing (No AI in Some Cases)
Phase 2: Core Implementation (AI Allowed)
Phase 3: Optimization
Known Problems
Who Gets This Interview?
What Are You Being Evaluated On?
How to Use the AI Effectively
What NOT to Do
How to Prepare
Practice in the CoderPad Environment
Practice Problems
Debugging in the CoderPad Environment
Your Mindset Going In
Other Companies Doing AI-Enabled Coding
Help Us Keep This Updated
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