There are lots of guides out there to FAANG interview processes. This one is the most thorough and the most detailed because it’s the only one made by interviewers for candidates – we spent hundreds of hours talking to dozens of current and former FAANG interviewers about their processes. Throughout this guide, you’ll see a bunch of direct quotes from these interviewers, where they describe the idiosyncrasies of each company’s process and bar in their own words. We’re fortunate to have them in the interviewing.io community, and we’re lucky that we can collaborate with them on this type of content. As you can imagine, they all requested to stay anonymous, but we want to thank them here, first and foremost.

Introduction to FAANG interviews

FAANG interviews are a gauntlet, but you can pass them even if you doubt yourself – interviewing is easier once you learn a company’s operating metaphor. George Lakoff (neuroscience and artificial Intelligence researcher) says that every human organization has a metaphor they operate as. If you ask an employee at a FAANG company about their metaphor, you’ll probably receive a blank stare in return. But if you look at the interviewing data of that same FAANG company, the metaphors jump out of the data set like bread out of a toaster.

This guide will walk you through all the FAANGs’ metaphors and the unwritten codes of those metaphors: what they reward, what they punish, and what they’re blind to. To show them you’re obviously a part of their tribe, model their metaphors and unwritten codes.

Metaphors aside, this guide will also walk you through the unglamorous logistics of every FAANG’s interview process so that you know how many steps there are, what those steps entail, and what kinds of questions they ask. Our goal is to have you walk in and be completely unfazed by the proceedings because you’re expecting them.

Of course, even with all the insider info in the world, if you’re not prepared for technical interviews, you will fail, so we’d be remiss if we didn’t share some useful insights about practice to help you on your journey.

About this guide

There are 3 steps to getting a strong offer at FAANG.

• Step 1: Get Interview.
• Step 2: Do Interview.
• Step 3: Negotiate.

Steps 1 and 3 are out of scope for this post, which is solely focused on Step 2.

Moreover, this guide is written for experienced, back-end leaning engineers – interview processes are usually different for juniors, but we won’t be getting into those differences. Finally, we won’t be getting into the differences in process for front-end engineers, SREs, etc. That said, if you’re targeting those roles, you’ll still get value out of this guide.

In Part 1 of this guide, we’ll highlight key similarities and differences between the FAANG companies, namely:

• Meta
• Apple
• Amazon
• Netflix
• Google
• Microsoft (they’re not officially FAANG, but we’re including them anyway – from now on, when we say “FAANG”, we mean Microsoft as well)

In Part 2, we’ll go through each company one by one and tell you how each of their processes work and how to prepare for each one.

If you’re planning on interviewing with multiple FAANGS, we recommend reading Part 1 first. If you'd prefer to skip straight to a company’s individual guide then just click on it in the table of contents to the left! Individual guides include more detailed information on company-specific coding interviews, behavioral interviews, and anecdotes from actual interview experiences.

All of these big tech companies share grueling interview processes, strong initial compensation packages, and above average benefits. They compete with each other for the same engineers. If tech has a food chain, they’re at the top. Most other tech companies copy or are influenced by what FAANG does.

There are also a number of myths about FAANG interview processes. Two big ones are that Amazon has the lowest bar, and Google has the highest bar. That’s not true; we have the data. The reality is that all of their bars are different. It’s not a linear comparison. It’s a multidimensional comparison. Because of that, it’s impossible to say something like, “The entire process at Google is harder than the entire process at Amazon.” They’re simply different processes.

And one common idea in big tech is that Google’s process is easier than Facebook’s. But you can see here: it really depends. After all, this person accepted the L4 role at Facebook because the compensation package was bigger than the role at Google for L6.”

Long story short: it’s complicated. That’s exactly why we wrote this guide: to demystify the differences, to decouple the 6-headed monster, and to demonstrate how to attack each head, one at a time.

3 tips for FAANG interview success

This is the ultimate insider’s guide to tech interviews at FAANG. However, even with all the insider info in the world, if you’re not prepared for technical interviews, you will fail, so we’d be remiss if we didn’t share some useful insights about practice to help you on your journey. Yes, we know we’re a practice platform, and, look, you don’t have to practice with us. But you should practice! Here’s why.

1. Do five practice interviews

Three different startups with robust data sets on software engineering interviews found eerily similar data points about what happens after you do five technical interviews. I worked at all three companies and saw this data myself.

• In a previous article about how to know when you're ready to interview at FAANG we shared that completing 5 technical interviews (mock interviews or real) doubles your chances of passing a subsequent technical interview.
• Triplebyte found that the chances of getting at least 1 offer increases with each onsite you do, up until 5 onsites. For each onsite completed after the 5th, your chances of getting an offer level off at 80-85%.
• Pathrise found that most of their engineers failed 4-5 onsites before they got an offer.

Mind you, these datasets were quite different: Triplebyte skewed towards folks with nontraditional backgrounds, interviewing.io inclined towards senior backend engineers, and Pathrise was mainly junior engineers. Despite that, the number five emerged across these data sets, and it’s clear that something happens after you complete five technical interviews. We can’t explain what yet. But the data is screaming in all caps: there is a there there. One more anecdotal point: these five interviews should ideally mimic the real thing as much as possible. For instance, if you want a FAANG job, but your five interviews are with startups that don’t ask algorithmic questions, you won’t get as much value. The more your practice can simulate the real game, the better.

2. If you’re unprepared, it’s OK to reschedule

If you’re unprepared for a FAANG interview, ask to reschedule. Not rescheduling is the number one mistake we see our users make! Getting an interview tomorrow (as opposed to 1-3 months from tomorrow) is, in most cases, no different to the company… but very different to you – if you fail, you might get frozen out for months. In the rare cases where rescheduling could hurt your chances because it’s a specific role for a specific team, your recruiter will tell you that. Either way, there’s no harm in asking.

3. A note about recruiter calls

Recruiter calls don’t differ much from FAANG company to FAANG company, so we decided to put everything about what to expect in a recruiter call in one place. If a recruiter call ever meaningfully deviates from this format, we’ll mention it. Otherwise, expect that it doesn’t.

A recruiter call is the first step of almost every process. In this call, a recruiter will ask you about your past experience, your salary expectations, and why you’re interested in that particular company. They will also ask you about your timeline (how soon you expect to accept an offer), how far along you are with other companies, whether you have outstanding offers, and so on.

In this call, it’s important to be able to succinctly talk about your past few positions, your major contributions at those positions (what did you do individually versus what did your team do) and their impact on the business. Remember that most recruiters don’t have a technical background and they’re not software developers, so it’s important to be able to describe your technical contributions in clear layman’s terms.

It’s also really important, at this stage, not to reveal your salary expectations, your salary history, or where you are in the process with other companies. We wrote a detailed post about salary negotiation that lays out exactly what to say when recruiters pressure you to name the first number. Just don’t do it – when you give out information this early in the process, you’re painting future you into a corner.

The Chaos Score

This section will give you a feel for how these companies’ processes differ. For now, don’t worry about how that translates into interview prep – we’ll cover that later when we describe how to prepare for each company.

Take a look at the chart below. In it, we rank the FAANGs on their “Chaos Score”. The more points a company has, the more chaotic they are.

In this context, we define “chaos” as the level of uncertainty and unpredictability that candidates can expect from the interview process and its outcomes. If a company consistently follows the same process, asks the same questions, and thoroughly trains their interviewers, they are not chaotic. If their process is completely non-standard, non-deterministic and subjective, they are chaotic.

As you can see, Apple and Netflix win the award for the most chaotic interview processes in FAANG. Microsoft gets 2nd place. Amazon gets 3rd, Google 4th, and Facebook 5th.

Chaos can be pain or pleasure depending on your tolerance for uncertainty. Chaos can also either impose a terrible detriment or prove a huge advantage in interviews specifically. For instance, candidates who have spent a long time grinding on LeetCode might prefer less chaotic companies. More chaotic companies can work for candidates for a few reasons: a) niche skill sets can be an easier match for niche interview processes, b) practical interviews are more likely, and c) the interview process shows them how the team they’d be joining actually operates.

How we calculated the Chaos Score for each company

To calculate each company’s Chaos Score, we picked four categories and graded each company on a 0 to 5 point scale in each category, where 5 means most chaotic and 0 means least.

1. The “north star” of their decision-making process (do they care more about “What”, “Why”, or “How”?)
2. If they have the same interviews company-wide or if the interview changes depending on the team with which you’re interviewing
3. How much they train their interviewers
4. How much they standardize their interview questions

We added up the points for each company, with a maximum possible Chaos Score of 20.

Deep dive into the FAANG Chaos Scores

The north star: “What”, “Why”, and “How” companies

By “North star”, we mean what a given company values most. As such, we grouped companies into 3 buckets: companies who primarily care about "Why", "How", and "What".

“Why” is the most chaotic because judging motivations is the most subjective approach. “How” is the second most chaotic because judging thought processes is the second most subjective. “What” is the least chaotic because judging end results is the least subjective method.

“Why” companies can’t agree on what a good “why” looks like because it’s a “gut feel / friend test”. Whom you consider to be a friend and who gives you a good gut feeling isn’t quantifiable. It’s completely subjective. “Why” companies are the most prone to bias. If you speak their language and model the behaviors they encourage, you’ll seem like a friend and give them a good gut feel. If you don’t, then you won’t.

If chaos is hell, then “Why” companies are raising hell for candidates and themselves.

“How” companies mainly care about your thought process: Okay, you didn’t get to the optimal solution, but what was the journey like? Google and Microsoft repeat this mantra again and again – they want to know how you think. You might get asked a really hard or specific question, but they don’t necessarily require an optimal answer to pass. They put far more weight on your ability to demonstrate a solid thought process.

“What” companies mainly care about your results, such as: Did you get to the optimal solution? Facebook and Amazon want you to get there, and fast. “What” is the most straightforward for candidates: simply get results as quickly as possible.

Team-dependent process

A Google or Facebook interview doesn’t change depending on the team you’re interviewing for. Both companies have one big, centralized interview process that’s completely divorced from which team you might end up on. If you do well in the team-agnostic process, there will be a team matching component after the onsite. You will NOT, however, be interviewing with your future coworkers.

(Note: Google is rumored to be changing to a team-dependent process, but we’ll leave that where it is for now.)

At Microsoft, Netflix, Apple, and Amazon, the process is team-dependent. You’ll not only be interviewing with the people that you’ll be working with, but there’s more chaos. Each team defines how they do things: the types of questions asked, the types of interview rounds, and even how they make hiring decisions.

Team-dependent processes are more challenging in the sense that you're more likely to get blind-sided; because each team has a different process, candidates are more likely to prep for X and get (a significantly different) Y.

Yet, team-independent processes are more challenging because of the machinery. Your interviewers are so far removed from you. That detachment affects how they treat, judge, and talk about you.

Think of it like this: At Netflix, Apple, Amazon, and Microsoft you’re interviewing with humans. At Google and Facebook, you’re interviewing with a machine.

Interviewer training

Facebook is the least chaotic company in this category because they have the most in-depth interviewer training in FAANG. Their process is rigorous and selective. Though most would-be interviewers pass within 6 months, some people who try never pass the bar to become an interviewer. Facebook is the only FAANG where this is true.

Facebook and Amazon put interviewer candidates through roughly the same things, but Facebook is more rigorous. For example, both will have similar modules interviewers go through in training. A module at Amazon is more likely to be a box to check: if you do it, you pass. At Facebook, you don’t pass simply for doing it: you pass by meeting a predetermined bar. Also, Facebook modules are more likely to have a rubric.

Google used to have a more in-depth interviewer training process than what they have now. For whatever reason, they began to skimp on their interviewer training roughly sometime in the 2010s. Now, Googlers can get a bit of training, but usually not as much as folks at Facebook or Amazon.

Netflix, Microsoft, and Apple do not train their interviewers; certain teams may be exceptions but there’s no company-wide required interview training. The day you start, you can start interviewing. That makes them the most chaotic in this category.

What this means for candidates is that the less training they put their interviewers through, the more likely you’ll have a bad interviewer. As the old saying goes: “Prepare for the worst, pray for the best.”

Interview question standardization

Companies that standardize their interview questions give interviewers less free reign; groups with no standardization have more free reign. At all companies, there will be interviewers who go rogue and deviate from the norm. Yet, each company has a norm.

Facebook wins again for being the least chaotic/most predictable. In behavioral rounds, interviewers at Facebook can ask whatever behavioral questions they want. However, in technical rounds, they can only ask pre-approved coding questions. They can also modify pre-approved coding challenges. That’s it.

Google is tied for the second least chaotic here; their interviewers have free reign in technical rounds. They have a large technical question bank, yet interviewers routinely make up their own ambiguous one-of-a-kind questions. In behavioral rounds, they can only ask or modify pre-approved questions.

Amazon is tied for second least chaotic, but for a different reason. Technically, there’s no standardization for any round (technical or behavioral). They do, however, tend to repeat questions from their internal question bank (which is not required for interviewers to use.)

Microsoft, Apple, and Netflix are the most chaotic companies. Each team decides what to ask. Questions tend to be customized to the hiring manager’s preferences, a senior individual contributor’s diligence, what this team works on day-to-day, or the specific domain this team is in.

Which FAANGs let you interview with different teams simultaneously?

Whether you get one shot or unlimited shots to land an offer changes how you prepare. Here’s the breakdown of which companies let you interview with multiple teams concurrently.

If you can interview with multiple teams concurrently, then they don’t have a cool down period. So, after you fail, you don’t need to wait at all to reinterview. Two of the biggest players only give you one shot to win, and at the remaining four your chances are unlimited.

Because their interview process is centralized, Google and Facebook are the only ones well-organized enough to not let candidates “double dip”.

In Part 2, we’ll give you a deeper feel for each company, and we’ll tell you what to do about it. Each company has its own section, and each section is organized into five subsections:

1. Interview process (including what to expect in the recruiter call and the phone screen)
2. What to expect in your interviews
3. How they make hiring decisions
4. Interview preparation resources