Interview Experiences at FAANG Companies (Shared Stories)

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FAANG – an acronym for Facebook (Meta), Amazon, Apple, Netflix, and Google – represents the most sought-after employers in tech.

Landing a job at a FAANG company is a dream for many beginners in software development due to the exciting projects, prestige, and generous compensation.

Consequently, FAANG interviews are highly competitive and thorough, designed to filter the best from a vast pool of applicants.

FAANG interviews typically involve a multi-stage process.

Candidates often go through an initial HR or recruiter screen, one or more technical phone interviews, and then an on-site (or virtual on-site) loop of multiple interviews.

These can include several coding rounds, a system design interview (for higher-level roles), and behavioral interviews to assess team fit and problem-solving approach.

In this blog, we’ll explore real interview experiences shared by candidates and break down each aspect of the FAANG interview – technical, system design, and behavioral – with key takeaways and preparation tips.

Shared Interview Stories from FAANG Candidates

Hearing from those who have been through FAANG interviews can provide valuable insights. Here are some real shared stories and takeaways from candidates who interviewed at Google, Amazon, Facebook (Meta), Apple, and Netflix:

Google: One candidate described a Google on-site involving five back-to-back interviews – four technical coding rounds and one focused on behavioral questions. The coding problems were challenging, delving into algorithms and data structures, and even included a dynamic programming question. The candidate emphasized that thorough preparation (practicing hundreds of coding problems) was essential to keep up with Google's rigorous questions. They also noted that Google’s interviewers expect you to explain your thought process clearly while writing clean code on a whiteboard or shared Google Doc.
Amazon: A candidate who interviewed at Amazon learned first-hand how critical Amazon’s Leadership Principles (LPs) are to the process. “Amazon is heavy on LPs... Can’t stress enough how important they are – don’t neglect your Leadership Principles prep!” one interviewee advised. In their experience, each interviewer spent the first 10-15 minutes on behavioral questions related to these LPs before diving into coding. There was even a dedicated behavioral round purely to assess attributes like ownership, customer obsession, and teamwork. On the technical side, Amazon’s coding questions ranged from an initial tough online assessment (a hard dynamic programming problem) to easier on-site questions (mostly medium-level LeetCode style problems). This candidate prepared by grinding through coding practice (around 280 problems) and studying system design fundamentals, which paid off in feeling comfortable during the interviews.
Facebook (Meta): An interviewee at Meta (Facebook) shared that the process felt as rigorous as Google’s – with challenging algorithm questions and an important system design interview for experienced roles. They had two coding rounds and one system design in their on-site. A key lesson they shared was about the behavioral interview: initially, they tried to apply the same structured STAR-method stories used for Amazon, but Meta’s interviewers wanted a more free-form conversation. The candidate realized that at Meta, authenticity and adaptability in behavioral answers mattered more than a perfectly rehearsed story. Once they adjusted to a conversational style and focused on clearly answering the question (rather than forcing a leadership principle into it), they had a much smoother experience. This person eventually landed the job at Meta, highlighting that technical excellence coupled with cultural fit is the winning combo.
Apple: Candidates who interviewed at Apple often note a mix of coding and design questions with a unique twist. Apple’s interview process tends to probe problem-solving creativity and your past project experience, reflecting the company's “think different” philosophy. One shared story involved a developer who was asked not only typical coding questions on data structures, but also to discuss how they would design a popular iOS app feature. Apple interviewers paid close attention to how the candidate approached the problem and the innovative solutions they proposed. The takeaway is that at Apple, beyond just getting the correct answer, demonstrating an innovative mindset and strong understanding of how things work (for example, how to optimize an app’s performance or improve a user experience) can set you apart.
Netflix: Netflix is known for its unique culture, and this shows up in their interviews. A candidate’s shared experience revealed that Netflix’s process placed comparatively less emphasis on algorithm puzzles and more on one’s past accomplishments and cultural fit. They were asked in-depth about their previous projects and how they handled challenges or made decisions, aligning with Netflix’s famous culture memo (values like freedom and responsibility). Technical discussions at Netflix still occur (especially relevant to the role – e.g. system design for backend roles, or coding tasks for data roles), but even those are often framed around real-world scenarios rather than abstract puzzles. The key takeaway from Netflix experiences is that you should be ready to discuss your work and choices in detail, and show that you thrive in a high-performance, self-driven environment.

These shared stories highlight that while each FAANG company has its own nuances, there are common threads: strong technical skills, good communication, and cultural alignment are all crucial. Now, let’s break down the experiences by the types of interviews.

Explore lessons from former FAANG engineers.

Technical Interview Experiences

Technical interviews at FAANG companies are typically coding-focused rounds where you solve programming problems in real time. These can be on a whiteboard, a shared Google Doc, or an online coding platform. The questions often involve data structures and algorithms – expect topics like arrays, linked lists, trees, graphs, dynamic programming, and system design algorithms (e.g. caching strategies). The difficulty can range from moderate to very challenging. For instance, one Google interview involved solving two problems in 45 minutes, requiring efficient solutions in terms of time complexity. Interviewers aren’t just looking for correct answers, but also how you think and communicate. Here are some common patterns and tips from various FAANG technical interview experiences:

Be prepared for DSA (Data Structures & Algorithms) challenges: Most coding rounds are essentially problems you’d find on LeetCode or HackerRank. Common patterns include traversals (e.g. tree or graph searches), dynamic programming for optimization, sorting and searching problems, and string manipulations. Brushing up on algorithmic fundamentals is a must.
Practice under interview conditions: You may be writing code on a blank whiteboard or a simple text editor without auto-complete. Try solving problems on paper or a whiteboard during practice. This helps you get comfortable writing syntax freehand and planning before coding.
Communicate your thought process: Almost every shared story stresses this. Speak out loud about how you’re approaching the problem – explain your ideas, consider different solutions, and clarify the problem requirements. Even if you get stuck, talking through it can prompt hints from the interviewer or at least show them your analytical approach.
Think in terms of examples and edge cases: Successful candidates often simulate a few test cases mentally or verbally after writing their solution. This shows you’re verifying your code and considering corner cases (e.g. empty inputs, very large inputs, etc.). It’s a common mistake to rush and code without testing your logic – one that can cost you even if your idea was correct.
Aim for clarity first, then optimality: It’s okay to start with a straightforward solution and then improve it if needed. Interviewers appreciate seeing how you optimize a naive solution. However, make sure your initial approach is clear and correct. Write clean code (good variable names, proper use of helper functions) as if it were production code – some interviewers, especially at companies like Google, value code quality and style.

Testimonial: One candidate who succeeded in landing offers mentioned that practicing about 280 coding problems beforehand made the on-site questions feel manageable. They noted that their Amazon on-site consisted of “easy-medium LeetCode questions”, which they breezed through thanks to extensive practice. Another interviewee who received offers from Meta and Amazon solved almost 500 practice problems over 6 months and still encountered surprises – “I got offers from Meta and Amazon, but was rejected by Google after bombing one easy question, which took me by surprise”. The lesson here is: practice is non-negotiable for technical rounds, and even then, stay vigilant and calm because interview pressure can make easy problems seem hard.

System Design Interview Experiences

For system design interviews, candidates (usually applying for mid-level or senior software engineering roles) are asked to design a high-level architecture for a large system.

Unlike coding rounds with one correct answer, system design problems are open-ended.

For example, you might be asked, “How would you design a service like YouTube?” or “Design the backend of a messaging app.”

Interview stories indicate that system design rounds evaluate your ability to handle ambiguity and make sound engineering decisions.

One common piece of advice is to start by clarifying requirements: ask about the goals of the system, scale (e.g. millions of users? real-time updates?), and any specific features to focus on.

Candidates who do well often break the problem down into core components (such as clients, servers, databases, caches, load balancers, etc.) and draw a high-level diagram (mentally or on the virtual board) of how these components interact.

Key concepts interviewers tend to probe include:

Scalability: How will your design handle millions of users or requests? Discuss techniques like load balancing, horizontal scaling (adding more servers), and using CDNs (for content distribution).
Data storage and caching: What database choices would you make (SQL vs NoSQL) and why? How would you use caching systems (like Redis or Memcached) to speed up responses?
Consistency and reliability: How do you ensure data is consistent across services? Would you use transactions, or how to handle failures so the system is fault-tolerant (e.g. redundancy, backups)?
Trade-offs: Perhaps most importantly, explain the trade-offs in your decisions. There’s no one “right” design – interviewers want to see you can reason about different approaches (for example, choosing a relational database for ease of querying vs a NoSQL for scalability, and what each trade-off means).

Candidates have shared that preparation is key here as well. Many recommend studying common architectures and principles (using resources like Grokking the System Design Interview or the book Designing Data-Intensive Applications).

Practicing by designing systems you’re familiar with (e.g. a URL shortener, an online bookstore, a social media feed) helps build a template you can adapt in the interview.

Testimonial: One engineer recounted that the system design interviews were actually “fun” because they could showcase their experience. In fact, they got so deep into one design at Meta that “the interviewer had to stop me from rebuilding Cassandra from scratch!”– a reminder not to get lost in the weeds. The takeaway is to cover the key aspects broadly first (so the interviewer is satisfied you’ve touched all critical points) before drilling down into any one detail, especially if time is limited. Engage the interviewer by asking if they’d like more detail on any part of your design – it makes the session interactive and ensures you focus on what they find important.

Behavioral Interview Experiences

Every FAANG interview loop also includes one or more behavioral interviews (sometimes called “culture fit” or “leadership” interviews).

Don’t be fooled – these are as important as the technical rounds. FAANG companies want to hire people who not only can do the job, but also mesh well with the company’s values and ways of working.

Amazon is famous for its rigorous behavioral evaluation through the lens of its 16 Leadership Principles.

Candidates have reported that Amazon interviewers will ask a lot of “Tell me about a time when…” questions.

For example, “Tell me about a time you disagreed with a team member's approach and what you did” or “Describe an unpopular decision you made and how you implemented it”.

To answer these, it’s best to use the STAR method (Situation, Task, Action, Result) to structure your story.

Amazon’s focus on leadership principles means they are looking for specific examples that demonstrate things like ownership, customer obsession, bias for action, invent and simplify, etc.

One interviewee mentioned that in each of their Amazon interviews, the first 15 minutes were devoted to behavioral questions on leadership principles – illustrating how much weight Amazon places on them.

Having 1-2 solid stories for each principle (drawn from your past experiences) can greatly boost your performance.

Google’s approach to behavioral interviews often comes under the umbrella of GCA (General Cognitive Ability) and “Googliness”.

Instead of a checklist of principles, Google might ask open-ended scenario questions to see how you think.

For instance, “How would you handle a project if you were asked to do something you’ve never done before?” or “Describe a situation where you had to solve a problem with limited information.” They are assessing your problem-solving approach, learning ability, and how you work with others.

The best way to tackle these is to think aloud, structure your response, and show a positive, solution-oriented mindset.

It’s okay to take a moment to gather your thoughts – just let the interviewer know you’re thinking. Google also values humility and collaboration, so demonstrate that you can communicate and work in a team (no braggadocio or cut-throat attitudes).

Meta (Facebook) interviews combine elements of both – they want to see leadership and an ability to thrive in a fast-moving environment.

A candidate who interviewed at Meta noted that being too formulaic (like reciting pre-memorized stories) can backfire. Meta’s interviewers encouraged a more conversational tone. They asked behavioral questions geared towards teamwork, dealing with ambiguous situations, and motivations.

The lesson here is to be genuine: know your experiences well enough that you can discuss them naturally and adapt to the conversation.

It’s fine to have a mental outline (STAR), but you should be able to riff on it comfortably rather than sound scripted.

Apple tends to look for culture add rather than culture fit – people who are passionate, creative, and team-oriented. You might get questions about innovation or how you handle deadlines and quality (since Apple values perfection in products).

Netflix, on the other hand, will likely grill you on their values (outlined in the Netflix culture memo).

Be prepared for frank questions; Netflix values transparency and courage, so they might ask things like, “Tell me about a time you gave or received tough feedback.”

Answering Netflix behavioral questions often means demonstrating maturity, self-awareness, and the ability to self-manage, since their culture empowers individuals with a lot of freedom.

How to approach behavioral interviews: In all cases, preparation and self-reflection are key. Think of several significant moments in your past (projects, team interactions, successes, failures) and draw out lessons or qualities from them. Practice answering common questions with those examples. And remember, the goal is to show you learn and grow from experiences. Even a failure story can impress if you highlight how it made you better. Finally, stay positive (no trash-talking previous employers or colleagues) and be consistent with what you’ve presented on your resume.

Check out the FAANG interview ultimate guide for software engineers.

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Lessons Learned & Common Mistakes to Avoid

From all these shared experiences, there are clear patterns in what works and what doesn’t. Here are some lessons learned and common mistakes to avoid for FAANG interviews:

Start preparing early and practice consistently: Many successful candidates spend months preparing – solving hundreds of coding problems, reviewing system design scenarios, and rehearsing behavioral answers. Cramming last-minute is a common pitfall. Instead, build up your skills over time, so you retain your problem-solving techniques under stress.
Don’t neglect any interview aspect: It’s a mistake to focus only on coding and ignore system design or behavioral prep (or vice versa). All rounds matter. For example, you could ace coding but if you stumble badly in behavioral, it might cost you the offer. Balanced preparation is key.
Avoid poor communication during coding rounds: One frequent mistake is coding in silence. Interviewers can’t read your mind – if you stay quiet, they don’t know why you’re doing what you’re doing. Always articulate your thinking. Similarly, don’t rush to code without clarifying the question. Take a minute to confirm you understand the problem requirements; ask questions if anything is unclear. This shows thoughtfulness and can prevent you from solving the wrong problem.
Learn to manage interview pressure: It’s easy to make silly mistakes under pressure – even on problems you find easy in practice. One candidate who solved 500 practice questions still got tripped up by an “easy” Google question due to nerves . The lesson is to keep calm, and if you feel stuck or panicked, take a deep breath, clarify your approach, and then proceed. Interviewers understand nerves; often they care more about how you recover than the mistake itself.
In system design, don’t jump into details too soon: A common error is diving into writing low-level components or code in a system design round. First outline the high-level plan. If you start discussing a specific database schema before agreeing on the overall architecture, you might lose the big picture. Instead, spend the first several minutes mapping out the top-level design. Only then drill into components where the interviewer shows interest.
For behavioral, avoid generic or dishonest answers: Interviewers can tell when you’re being overly vague or telling a rehearsed story that doesn’t directly answer the question. Be specific (“In my last project, when we had a sudden outage, I...”) and show genuine reflection. Also, never fabricate stories – good interviewers will probe for details and can catch inconsistencies. It’s perfectly okay if your example is from a school project or a small internship; what matters is what you did and learned.
Use the STAR method but don’t sound like a robot: Structure is helpful, especially for Amazon, but if you memorized a script, it can sound unnatural. Practice enough that you remember the key points of your story, but you can adapt to exactly what’s asked. If a question is slightly different than you expected, don’t force an unrelated prepared answer – tweak your story or choose a more fitting example on the fly.
Ask for feedback (when possible) and learn from each attempt: Not every interview will be a success, and that’s okay. Use each interview experience as a learning opportunity. If you don’t get an offer, reflect on which questions stumped you or where you felt uncomfortable, and focus your practice on those areas for next time. Many people improve and eventually crack a FAANG interview on a second or third attempt.

Final Thoughts

Interviewing at FAANG companies is undoubtedly challenging – it tests a wide range of your abilities from coding chops to design sense to interpersonal skills.

The shared stories above illustrate that preparation, persistence, and a growth mindset are your best allies.

Each person’s path is different: some face failure (even multiple rejections) before finally landing their dream job, while others might strike gold on a first try.

What matters is learning from each experience and not giving up.

To summarize the journey: master the fundamentals (for coding and design), practice extensively, and be true to yourself in behavioral interviews.

Go into each interview round with confidence, knowing you’ve done your homework.

And if things don’t work out, remember it’s not the end – take the feedback, polish your skills, and try again.

The process can be long and demanding, but as many FAANG employees will attest, the reward of working at a company you admire is well worth the effort.