I’ve been watching the FAANG interview prep industry for a few years now, and I think there’s a version of this advice that gets repeated so often it’s become its own kind of noise. “Do 200 LeetCode problems. Study system design. Prepare 8 to 10 STAR stories.” All true. Also not enough to distinguish anyone, because at this point, everyone applying to Google or Meta or Amazon has read the same blog posts and done the same LeetCode grind.
What actually decides these interviews, in my view, is different from what most prep resources emphasize. I could be wrong about this. But here’s what I’ve observed.
What “FAANG” means in 2026
The acronym started as Facebook, Apple, Amazon, Netflix, Google. Netflix stopped doing mass engineering hiring in the same way. Meta rebranded. The category expanded. The more useful framing now is “companies that use a structured, multi-round interview process with dedicated algorithms, system design, and behavioral rounds, and pay in the top 5% of the market.”
That includes Meta, Amazon, Apple, Google, and Microsoft (sometimes called MAANG now). It also includes Stripe, Databricks, Snowflake, Coinbase, and a few others that have adopted similar processes. The preparation is mostly transferable across these companies, with company-specific flavor in the behavioral and system design components.
The LinkedIn Economic Graph data shows that hiring at these companies contracted sharply in 2022 and 2023 and has recovered unevenly since then. Entry-level hiring, in particular, is nowhere near its 2021 peak. The acceptance rates are as low as ever. That context matters because it affects how you think about the effort required and the return you should realistically expect.
The part of LeetCode prep most people do wrong
The standard advice is to grind 150 to 200 problems. This is correct in rough direction and wrong in emphasis. The problem isn’t the number. The problem is that most people grind easy problems until they feel comfortable with easy problems, and then intermediate problems until they feel comfortable with those, and they never actually get uncomfortable with hard problems before the interview.
Hard problems at FAANG aren’t just harder versions of medium problems. They require holding multiple concepts in your head at once, recognizing that a problem requires a combination of two data structures, or seeing a non-obvious reduction. The way to get better at this is not to do more medium problems. It’s to attempt hard problems, fail at them, study the solution, and attempt similar hard problems again.
This is genuinely unpleasant. Most prep resources don’t say this because “this is going to be unpleasant for a while” doesn’t make good course marketing copy. But if you can comfortably grind mediums and then hit a wall on hard problems, that’s a calibration failure in your prep, not a talent ceiling.
The 2024 Stack Overflow Developer Survey data on interview experiences shows that candidates who do deliberate hard-problem practice report feeling better prepared for on-site rounds than candidates who focus primarily on volume of easy/medium problems. That’s a self-reported metric with obvious biases, but it tracks with what I’ve heard from people who’ve gone through these loops.
System design: the actual differentiator at mid-level+
For anyone interviewing at senior or above, system design is where the offer usually gets decided. Not because the algorithms round is easy at that level, it isn’t, but because more candidates pass the algorithms round than fail it, and the system design round is where the real signal comes from.
The common mistake is treating system design prep like algorithms prep: study 8 to 10 classic designs (URL shortener, rate limiter, feed ranking, distributed cache, etc.) and learn them well. This is necessary but not sufficient. What interviewers are actually evaluating is how you reason under ambiguity, how you handle trade-offs, and whether you can lead a conversation without the interviewer having to drag you along.
The candidates who do well in system design interviews tend to do three things differently. They clarify requirements before designing anything, explicitly stating what they’re choosing to prioritize. They name trade-offs out loud rather than making silent choices. And they ask for feedback mid-interview – “I was going to go deep on the database schema here, does that seem like the right level of detail?” – which shows collaboration rather than defensiveness.
I’ve seen solid engineers fail system design rounds not because they couldn’t design the system, but because they designed quietly and left the interviewer with nothing to react to. The conversation is part of the evaluation.
Behavioral interviews: the part people underestimate
Amazon’s leadership principles are the most formalized version of this, but every major tech company uses behavioral interviews to assess whether you’ll fit their operating culture. The standard prep advice (prepare 8 to 10 STAR stories) is correct. But there are two failure modes in how people prepare these stories that show up constantly.
The first is round numbers. “I increased performance by 50%” or “I reduced churn by 30%.” Real engineers know the exact numbers, or at least the approximate range with the right caveats. “We reduced P99 latency from around 800 milliseconds to about 210 milliseconds, though that’s before we added the new feature set that pushed it back up a bit” sounds like a real person talking about real work. “I improved system performance by 50%” sounds like something you wrote on a résumé and then memorized.
The second failure mode is stories that are too polished. They’re structured, coherent, positive, and they leave out any indication that you were uncertain, wrong about something, or had to change course. Real projects have those moments. If your story doesn’t, either you’re leaving something out or you learned nothing, and neither is a good signal.
How AI practice tools fit into this (and where they don’t)
There’s been a wave of AI interview prep tools over the past two years, including Craqly. I think they’re genuinely useful for one specific thing and less useful for others.
They’re useful for behavioral practice. The thing that makes behavioral interviews hard isn’t knowing the STAR format. It’s saying your answers out loud, with appropriate pacing, under something resembling real-time pressure. Practicing with an AI tool that can prompt you with follow-up questions (“can you tell me more about the trade-off you made there?”) gives you reps that are qualitatively better than rehearsing alone in your head.
They’re less useful for system design, because system design is fundamentally a conversation with a specific human who has specific preferences and a specific reaction to what you’re saying. An AI can give you structure, but it can’t replicate the social dynamics of a real system design interview. For that, you want mock interviews with actual engineers, which typically run $100 to $200 per session for professional services, or you can arrange them informally with people in your network.
For algorithms, AI tools can explain solutions and catch syntax errors, but the actual skill-building happens through independent problem-solving, not through AI-assisted walkthroughs. The struggle is the point.
A few things that genuinely help that aren’t in most guides
Apply to your target companies in batches, not sequentially. If you get a phone screen at Google, try to get phone screens at Meta and Amazon in the same week. The rhythm of practice in a real interview context transfers between companies in ways that solo practice doesn’t. Being “warm” on the interview circuit is a real phenomenon.
Record yourself doing mock problems. Watching yourself think out loud is uncomfortable and also extremely useful. Most people discover that they explain less than they think they’re explaining, or that they go quiet for 90 seconds in ways that would make an interviewer anxious.
Accept that some of this is luck. You might draw a problem you’ve seen before. You might draw a problem in an area where you’re weakest. You might interview on the one day when the hiring bar just shifted. Candidates who fail FAANG interviews on the first try and then pass on a second attempt often haven’t changed much about their preparation. Variance is real. That’s not a reason not to prepare. It’s a reason not to over-conclude from a single result.