Using AI in Meta’s AI-assisted coding interview (with real prompts and examples)

In October 2025, Meta began piloting an AI-enabled coding interview that replaces one of the two coding rounds at the onsite stage. It’s 60 minutes in a specialized CoderPad environment with an AI assistant built in. It’s highly likely that this round will be rolled out for all back-end and ops-focused roles in 2026.

While Meta’s official prep materials will tell you that AI usage during this interview is optional and will have no bearing on the outcome, in practice, that’s not entirely true, and we believe that using AI properly will give you an edge. To wit, this post is a practical walkthrough of how AI fits into these interviews, using concrete examples of prompts, code, and AI outputs, and showing how to integrate them without sacrificing judgment.

Stop memorizing STAR for behavioral interviews. Start selecting better stories.

"Why don't you tell me about a time you received constructive feedback?"

Simple question. Staff-level candidate. Should be easy.

"I was leading development of a new service at Amazon. Tight deadlines, exciting technical challenges. My role included end-to-end delivery and then transition to the next project. I prioritized shipping the core functionality. Built it, tested it, launched it. The service worked technically. But during my next review cycle, my manager flagged it. The team struggled without proper docs. The handoff left gaps. I learned to treat documentation and handoff as first-class requirements, not afterthoughts. Now I add them as explicit tasks in the backlog from day one when planning projects."

Perfect CARL (or STAR) format. Clear context. Specific actions. Measurable results. Concrete learning.

Rejected on behavioral.

Why? Because at Senior+ levels, your story selection matters more than your story structure.

Becoming an MLE at FAANG: What you need to know to know about MLE roles and interviews at Google, Meta, and other top companies

I’m Shivam Anand, currently leading machine learning engineering (MLE) efforts at Meta, focused on integrity, recommendation, and search systems. Over the past decade, I’ve applied state-of-the-art ML to some of the toughest challenges in big tech—from scaling anti-abuse systems at Google Ads to rebuilding ML systems for Integrity enforcement at Facebook.

I’ve seen first-hand how the nature of ML work varies massively across team types and career paths. This guide is my attempt to map that space for others navigating (or considering) careers in ML—especially those targeting roles in big tech. I will cover different ML team types, the kinds of roles you’re likely to see on those teams, how interview processes vary for ML roles, and how to make the lateral move from a software engineering role to an MLE one.

Stop trying to make recruiters think, or, why your resume is bad and how to fix it

Years ago, Steve Krug wrote a book about web design called Don’t Make Me Think. It’s a classic, and the main point is that good design should make everything painfully obvious to users without demanding anything of them.

Resumes are just the same. Your resume shouldn’t make recruiters think. It should serve up the most important things about you on a platter that they can digest in 30 seconds or less. We've said before that spending a lot of time on your resume is a fool's errand, but if you’re going to do something to it, let’s make sure that that something is low-effort and high-return. Here's exactly what to do.

Why resume writing is snake oil

A lot of other platforms offer resume reviews or help with writing resumes for $$. We don't do it, despite a lot of our users asking for this feature. The reason I've refused to build them is because, simply put, resume writing is snake oil. Why? Because recruiters aren't reading resumes. If you don't have top brands, better wording won't help. If you do have top brands, the wording doesn't matter.

It's OK to postpone your interviews if you're not ready

At interviewing.io, we’ve seen hundreds of thousands of engineers go through job searches, and the biggest mistakes we see people make are all variations on the same theme: not postponing their interview when they aren’t ready. In most situations, there is no downside to postponing. In this post, we'll tell you what to do and say.

How well do LeetCode ratings predict interview performance? Here's the data.

Have you ever wondered if you should spend more time on LeetCode, participate in those contests, or focus on solving harder problems? A popular Reddit post suggests you need 700+ questions and a LeetCode rating between 1800-2000 to pass FAANG interviews. Is this really what the data supports? To answer these questions and more, we looked at our users' LeetCode ranks and ratings and tied them back to interview performance on our platform and whether those users worked at FAANG.

In this post, we’ll share what we’ve learned.

How to get in the door at top companies: Cold outreach to hiring managers. Part 2 of 2.

In part 1 of this post, we analyzed different ways to get into companies along two axes: effectiveness and how much control you actually have.

The channel that maximizes both effectiveness and control is cold outreach to hiring managers (not recruiters!). And yet, most people do this type of outreach incorrectly. In this post, we'll tell you exactly what to do and what to say to reach out to hiring managers at top companies and get responses.

How to get in the door at top companies: a data-driven, practical guide for software engineers. Part 1.

interviewing.io is an anonymous mock interview platform — we help engineers prepare for technical interviews. In this market, many of our users are struggling with getting in the door at companies, so we ran a survey to see what’s worked well and what hasn’t, in today’s difficult climate.

Not surprisingly, warm referrals are the best way in. On the other hand, agency recruiters are clearly the worst. But not all channels are created equal. Some, like recruiters contacting you, you have minimal control over, and whether you get contacted is largely a function of whether you have top brands on your resume or belong to an underrepresented group. With others, like reaching out to hiring managers, you are fully in control of your destiny. Here's how to make the most of a difficult landscape.

I’ve never written code, but I just passed a Google system design interview.

My name is Kevin. I am not and have never been a software engineer. I have never written or tested a single line of code, and I have never even worked as a PM. Despite that, I was able to pass a Google system design interview.

I had just finished working on a system design interview guide and learned a LOT from doing that, but I also learned a few tricks along the way. If these tricks helped me pass, then imagine what you’ll be able to do with them.

How software engineering behavioral interviews are evaluated at Meta (from an ex-Meta manager)

Hi, I’m Lior. I spent close to five years at Meta as a software engineer and engineering manager. During my time there I conducted more than 150 behavioral interviews. In this post, I’ll be sharing what Meta looks for in a behavioral interview, and how we evaluated candidates.

Does communication matter in technical interviewing? We looked at 100K interviews to find out.

The interviewing.io platform has hosted and collected feedback from over 100K technical interviews, split between mock interviews and real ones. It’s generally accepted that to pass a technical interview, you have to not only come up with a solution to the problem (or at least make good headway), but you also have to do a good job of articulating your thoughts, explaining to your interviewer what you’re doing as you’re doing it, and coherently discussing tradeoffs and concepts like time and space complexity. But how important is communication in technical interviews, really? We looked at the data, and it turns out that talk is cheap. Read on to find out how and why.

We analyzed 100K technical interviews to see where the best performers work. Here are the results.

At interviewing.io, we’ve hosted over 100K technical interviews, split between mock interviews and real ones. As it happens, we know where our users currently work – they tell us that when they sign up. Given that we have this data AND given that we know how well people do in their interviews, we thought it would be interesting to see which companies’ engineers are especially good at technical interviews. Our resulting top ten lists are in this post!

How do I know if I’m ready to interview at FAANG?

Recently, someone asked us how you know you’re ready to succeed in a Facebook/Amazon/Apple/Netflix/Google (FAANG) interview. It’s an interesting question, and one I’m sure many of you job seekers out there are wondering. Internally, we have our own beliefs, but we wanted to see if we could answer this question more objectively. So we set off on a journey to acquire data to try answering it.

I’ve conducted over 600 technical interviews on interviewing.io. Here are 5 common problem areas I’ve seen.

I recently conducted my 600th interview on interviewing.io (IIO). I’d like to share lessons learned, why I approach interviews the way that I do, and shed some light on common problem areas I see happen in technical interviews. Every interviewer on the platform is different, and so your results may vary. We have some excellent folks helping out on the platform, and have a wonderful community working to better ourselves.

6 red flags I saw while doing 60+ technical interviews in 30 days

What is the one thing you would look out for if you had to join a company? Sometime between January and February 2020, I wanted to change jobs and was looking to join a new company. This, among other reasons, led me to embark on a marathon of technical interviews – 60+ technical interviews in 30 days. Doing that many number of interviews in such a short time meant I had an interesting mix of experiences from the various companies I interviewed with, each with their unique culture and values that often reflected in the way their interviews were conducted, intentionally or not.