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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 a few models (GPT-4o mini, Claude 3.5 Haiku, Llama 4 Maverick) and you can switch between them, though I'd recommend getting familiar with one ahead of time.
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 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.
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.
The approach is consistent across tests:
- Uncomment a failing test
- Run it and check expected vs. actual output
- Fix the underlying code to make it pass
- Move to the next test
If you're using Python, make sure you're comfortable with unittest and reading its output, as that's the testing framework used.
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.
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. Some candidates barely touch it. Others use it heavily for scaffolding. Both approaches work if you understand the solution.
Use the AI to accelerate your workflow, but review everything it produces. If you blindly accept its output, the interviewer will notice.
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
Recruiters are offering candidates a link to a practice environment before the interview so they can gain familiarity with the AI chat panel, model switching, and code execution. If they did not send it to you proactively, just ask your Meta recruiter for the practice link.
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.
My suggestion would be to 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.
Lastly, make sure you keep the interviewer in the loop, even while prompting. Say out loud 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." This running commentary shows them your thought process. You don't need to fill every second with talk, but periodically summarize your plan or progress.
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 heavier-weight models (like Sonnet 4.5 or Opus) compared to Meta's lightweight models.
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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