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Meta's AI-Enabled Coding Interview: How to Prepare
By Evan King
With the relentless release of ever improving LLMs, LeetCode has long been believed to be on its dying days (I'm surprised it is still around honestly). Why test a candidate's ability to solve a small coding problem that an LLM can solve in seconds? Up until now, the big companies have been really slow to adapt, but Meta has made the first move (and I suspect others will follow).
Meta has rolled 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 recent candidates as well as friends from within Meta to get a firmer grasp on what the new AI-coding interview entails so you can be better prepared. I'll also do my best to keep this blog up to date as we learn more.
Here's what we know so far.
What Is It?
You get 60 minutes in a CoderPad environment with an AI chat window built in just like Cursor or Github Copilot. The system offers multiple models that you can switch between during the interview:
- GPT-4o mini
- GPT-5
- Claude Haiku 3.5
- Claude Haiku 4.5
- Claude Sonnet 4
- Claude Sonnet 4.5
- Gemini 2.5 Pro
- Llama 4 Maverick
I'd recommend getting familiar with one model ahead of time, though candidates report that some models work better for different tasks. 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 will work through one extended problem with multiple stages. You'll receive a mini-project or multi-file codebase that might include several Python files, a requirements.txt, and possibly some data files. You are expected to read it, modify it, extend it, and make it work.
The supported languages are Java, C++, C#, Python, and TypeScript. You can choose your preferred language from this list—confirm with your recruiter if you have a preference.
The problems usually involve some mix of:
- Building something from scratch based on a spec
- Extending existing code with new features
- Debugging broken implementations
You might debug a base implementation, then extend it with a new feature, then handle edge cases the tests expose. The experience is designed to mimic modern, real world development where you can ask the AI questions and rely on it as a pair programming assistant.
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 want to see that you're still in charge, but can leverage the AI as an accelerator, not a replacement.
Interview Flow & Timing
Here's how the 60 minutes typically break down:
Setup & Environment (~5 minutes) You'll spend the initial 5 minutes getting familiar with the CoderPad interface and AI chat panel. The interviewer will confirm you can use AI as much as you want and clarify whether you can use external files or prompts (typically no).
Problem Explanation (~5-10 minutes) The interviewer reads the problem statement and you ask clarifying questions about requirements, constraints, and expected output. This is encouraged and shows good problem-solving instincts.
Codebase Exploration (~5-10 minutes) You'll review the existing code structure and helper functions, examine test files to understand expected behavior, and run initial tests to see what passes and what fails.
Debugging Phase (~5-10 minutes) Fix bugs in existing helper functions, using AI to help diagnose test failures. Get helper function tests passing before moving to main implementation.
Implementation Discussion (~5 minutes) Consider different algorithmic approaches like greedy, backtracking, or dynamic programming. The interviewer may challenge your approach to test understanding, so be ready to defend your choice or adapt based on the problem constraints.
Main Implementation (~15-20 minutes) Implement the core solution using AI strategically. Test incrementally as you build, debug issues as they arise, and iterate until tests pass.
Complexity Analysis (~5 minutes) Be prepared to explain the time and space complexity of your solution and discuss trade-offs and potential optimizations.
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.
Example Problem: Maze Solver
One confirmed problem involves working with a codebase for creating, solving, parsing, and printing a maze. The task is structured around getting four test files to pass:
- Parts 1-2 (debugging): Get familiar with the codebase and fix basic issues like incorrect path printing and missing visited-node tracking in DFS/BFS
- Parts 3-4 (feature implementation): Add functionality to the BFS algorithm—think LeetCode problems like "find all keys" where you need to track state while traversing
You don't necessarily need to complete all four parts to pass. Multiple candidates have reported passing with 3/4 parts solved, especially if they can articulate how they'd fix the remaining issues.
If you're using Python, make sure you're comfortable with unittest and reading its output, as that's the testing framework used.
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.
You can see the full list of user reported Meta AI-enabled coding problems below:
Live, up-to-date
Most commonly asked AI-Enabled Coding questions
Meta
Who Gets This Interview?
This is being used for Software Engineer (SWE) and Engineering Manager roles as part of the onsite loop. 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 another traditional coding interview—one classic algorithm problem interview (no AI) and one AI-assisted interview. Your recruiter will tell you which is which ahead of time.
As of November 2025, Meta has transitioned to the AI-enabled coding format for many roles. Recent recruiter confirmations indicate this is no longer a pilot program for M1 (engineering manager) roles and is becoming the standard. However, some candidates may still receive traditional coding rounds. Always confirm the format with your recruiter during your initial call.
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.
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.
Use the AI to accelerate your workflow, but review everything it produces. Don't blindly accept suggestions—the interviewer wants to see you catch mistakes and make informed decisions about what code to keep.
Interviewer Interaction
The interviewer isn't just silently watching. Recent candidates report interviewers who actively guided them when stuck and helped them connect the dots. If you're spinning your wheels, don't be afraid to think aloud about what's blocking you—the interviewer may offer a helpful nudge. That said, don't expect them to solve the problem for you. They're testing whether you can get unstuck with minimal guidance.
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 problem called "Wordle Solver" that you can access in your preferred language. The practice environment shows the exact folder structure and interface you'll use in the real interview. Spend significant time here—candidates report that familiarity with CoderPad's quirks and AI model behavior makes a huge difference under time pressure.
Meta CoderPad Practice Environment
The practice problems help you get comfortable with:
- The AI chat panel and model switching
- Running tests and reading test output
- The folder structure and file navigation
- Environment quirks like the output panel not auto-clearing
Practice Problems
The best practice problems are LeetCode's design section. These are practical coding problems where you build multi-class systems with state management, edge cases, and multiple methods working together which is the closest I have found to the types of questions you will be asked in the interview.
Work through these 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.
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.
Keep the interviewer in the loop by thinking aloud. While you ask the AI for code generation, use those seconds to explain your approach or start writing a different part of the code. Say what you're doing: "I'm going to implement the core data structure first, then add the helper methods... I'm asking the AI to generate the skeleton for the cache eviction logic so I can focus on the lookup method." You don't need to fill every second with talk, but periodically summarize your plan or progress. If typing something yourself is faster than formulating a prompt, just do it—60 minutes goes fast.
Other Companies Doing AI-Enabled Coding
Meta isn't alone. Other companies are experimenting with similar formats:
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.
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 interview format is still evolving across the industry, so companies are tuning difficulty and expectations as they gather more data.
Help Us Keep This Updated
This interview format is brand new and still evolving. If you've gone through Meta's AI-enabled coding interview, or if you have insider knowledge about how it's changing, 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. Lastly, 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
Example Problem: Maze Solver
Who Gets This Interview?
What Are You Being Evaluated On?
Interviewer Interaction
How to Prepare
Practice in the CoderPad Environment
Practice Problems
Debugging in the CoderPad Environment
Other Companies Doing AI-Enabled Coding
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