How to Crack FAANG Coding Interviews?

The pattern-based approach that transforms coding interviews from impossible puzzles into recognizable problems

Picture this: You can architect complex recommendation systems that serve millions of users, but you're stumped by a "medium" LeetCode problem about finding the longest substring without repeating characters. Sound familiar?

In my previous posts, I've deconstructed the Machine Learning Engineer interview at Meta, demystified the ML System Design interview, and provided a real-world guide to ML careers in 2025. Here's one uncomfortable truth I learned during my Meta interview loop: even for specialized/generalist ML roles, the process is still gated by standard coding interviews where you solve LeetCode-style problems under pressure.

For many brilliant ML practitioners, this feels like an artificial, frustrating hurdle. The good news? After helping dozens of ML engineers navigate this process (and going through it myself), I've discovered that coding interviews are a learnable skill with predictable patterns.

This post will give you a systematic, 8-12 week roadmap to conquer FAANG coding interviews — not "just grind LeetCode" advice.

Here's what you'll walk away with:

• A pattern-based study method that makes 80% of problems feel familiar

• Concrete milestones to track your progress

• Company-specific strategies and timeline

• The exact interview execution framework I use

Let's dive in.

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Part 1: The Landscape - What Are They Really Testing?

Before even diving into preparation strategies, let's decode what FAANG companies are actually evaluating. I would make it very clear that they aren't just testing your ability to memorize solutions. They use these challenges as a proxy to measure several key skills in a standardized way:

1. Problem-Solving & Analytical Skills: Can you take an ambiguous problem, break it down, identify constraints, and formulate a logical approach?

2. Data Structures & Algorithms Knowledge: Do you have the fundamental toolkit? In the Meta MLE loop, the coding rounds are pure DSA, with no ML flavor. Your knowledge of hash maps, trees, and graphs is what's on trial.

3. Code Fluency & Cleanliness: Can you translate your logic into clean, readable, and bug-free code in a reasonable amount of time?

4. Communication: Can you articulate your thought process, explain trade-offs (time vs. space complexity), and incorporate feedback?

5. Verification & Testing: Do you proactively test your solution with edge cases, or do you wait for the interviewer to find your bugs?

Understanding these five evaluation criteria is essential for targeted preparation. When you know that communication and problem-solving approach matter as much as the final solution, you can structure your practice accordingly. Many brilliant engineers with strong technical backgrounds fail not because they lack knowledge, but because they've never practiced articulating their thought process or methodically testing their code under interview conditions.

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Part 2: The Game Plan - A Phased Approach

From my own experience, random LeetCode grinding doesn't work well. I have tried that and realized that I just end up wasting a lot of time whenever I do that. You need a system. Here's my four-phase approach that works for me:

Phase 1: Rebuilding the Foundation (1-2 Weeks)

Avoid the temptation to jump directly into "Hard" problems. Your time is better spent mastering "Medium" level questions, as these form the core of most interviews—in fact, "Hard" problems are a rarity. If your fundamentals are rusty, you'll just be building on sand. Before tackling interview-level problems, go back to the basics. For each major data structure and algorithm, you should be able to:

• Explain what it is and its common use cases.

• Implement it from scratch (or at least its core logic).

• Know its average and worst-case time and space complexity for key operations.

Before diving into problem-solving, I highly recommend getting a copy of "Cracking the Coding Interview" by Gayle Laakmann McDowell. This book is invaluable for reviewing fundamental concepts in bite-sized, digestible chunks. Each chapter provides clear explanations of data structures and algorithms with practical examples, making it perfect for quickly refreshing your knowledge before moving into active problem-solving practice.

Your Core Topics Checklist:

• Data Structures:

  • Arrays & Strings

  • Hash Maps / Dictionaries

  • Linked Lists (Single, Double)

  • Stacks & Queues

  • Trees (Binary Trees, BSTs, Tries)

  • Heaps (Priority Queues)

  • Graphs (Adjacency List vs. Matrix)

• Algorithms:

  • Sorting (know how Merge Sort works)

  • Searching (Binary Search)

  • Tree & Graph Traversal (BFS, DFS)

  • Recursion & Backtracking

  • Dynamic Programming

These topics are more than enough. No you don’t have to understand Red Black Trees/ AVL trees and what not. Mostly you will be really unlucky if you were asked that in the interview. As I tell my mentees, if you get questions from these topics assume that it is your bad day and apply again after 6 months.

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Phase 2: Mastering the Patterns (4-6 Weeks)

Here's where the magic happens. With your foundation solid, you're ready for the most powerful insight in interview preparation: most coding problems aren't actually unique puzzles—they're variations on a small set of recurring themes.

Think of it like learning to play chess. A beginner sees each position as completely new and overwhelming. But a master recognizes patterns: "This is a fork," "That's a pin," "Here's a back-rank mate threat." The board position might be different, but the underlying tactical motifs repeat endlessly. Coding interviews work the same way.

The "Pattern-First" Study Method: For each pattern below, solve 5-10 medium-difficulty problems until you can spot it instantly and implement it fluently.

1. Sliding Window: Look for keywords like "subarray," "substring," or "contiguous" - these problems want you to find optimal sequences by expanding and contracting a window with two pointers.

• 3. Longest Substring Without Repeating Characters

• 424. Longest Repeating Character Replacement

• 76. Minimum Window Substring

• 567. Permutation in String

2. Two Pointers: Perfect for sorted arrays, finding pairs that sum to targets, or palindrome checks. Start from opposite ends and move pointers based on comparisons, but remember this only works w