30 Meta LeetCode Interview Questions for 2026

See the 30 Meta LeetCode interview questions most often reported in 2026, plus the five-round loop, AI coding phase, and prep plan.

Meta LeetCode Interview Questions: 30 Most Asked (2026)

Meta's coding rounds still run on LeetCode-style problems. That hasn't changed. What has changed is the format around them — a new AI-enabled coding phase, updated tooling, and a loop structure that tests communication as hard as it tests algorithms. This post breaks down the 30 Meta leetcode interview questions candidates report seeing most often, organized by pattern, plus the prep strategy that actually works in 2026.

No fluff. No "top 500 problems you should definitely grind." Just the patterns Meta tests, the problems that represent them, and how to prepare efficiently whether you're a new grad or a staff-level candidate.

How Meta's coding interview is structured in 2026

The five round loop

Meta's on-site loop is five rounds, each 45 minutes. Two are coding, one is behavioral, one is system design, and one is product design — though the mix shifts by role. Infrastructure roles often swap product design for a second system design round. Coding rounds use CoderPad; design rounds use Excalidraw.

The coding rounds carry significant weight, but they don't exist in isolation. Behavioral and design performance matters too, especially at E4 and above.

The AI enabled coding round

Starting in October 2025, Meta began rolling out an AI-enabled coding interview format. Not every candidate gets it yet, but it's expanding. The format runs in three phases: bug fixing in an existing codebase, core feature implementation, and optimization. Candidates can use AI models — GPT-4o mini, GPT-5, Claude, Gemini, Llama 4 — but the AI may be intentionally weakened during the interview.

What interviewers are actually watching: do you verify the AI's output, or do you accept it without looking? Problem solving, verification, and communication are the scoring signals. Being able to use AI is not the same as being able to lean on it.

The Meta LeetCode question bank — what candidates actually see

There's a community-reported pool of roughly 150–200 Meta-tagged questions on LeetCode. That figure comes from candidate discussions on r/leetcode — it's not an official number, and no single verified public list exists.

The practical filter: sort LeetCode's Facebook-tagged questions by frequency, and solve the top 100 from the last six months. That's the advice from candidates who've been through the loop recently, and it's a better use of time than grinding every tagged problem. LeetCode's own Top Interview 150 study plan is a reasonable structural baseline if you're starting from scratch and have 3+ months of prep time.

The 30 problems below are the ones that show up most consistently in candidate reports, organized by pattern — because Meta tests patterns, not random problems.

Meta LeetCode interview questions by pattern

Arrays and two pointers

Meta loves array manipulation problems that test whether you can think in O(n) time without reaching for a sort or a hash map first.

• Two Sum — hash map lookup for complement pairs; the canonical warm-up
• 3Sum — sorting + two pointers to avoid duplicates; tests careful pointer management
• Container With Most Water — greedy two-pointer approach; tests recognizing when brute force is unnecessary
• Move Zeroes — in-place array partitioning; tests clean swap logic
• Merge Sorted Array — reverse-fill from the end; tests edge-case awareness with pre-allocated space

Sliding window and substrings

String manipulation appears frequently in Meta rounds, likely because so much of Meta's product surface involves text processing, search, and feed ranking.

• Longest Substring Without Repeating Characters — classic variable-width sliding window
• Minimum Window Substring — harder window with character frequency tracking; a common E4+ problem
• Find All Anagrams in a String — fixed-width window with frequency comparison

Trees and graph traversal

Graph and tree problems are heavily represented — unsurprising for a company whose core product is a social graph.

• Binary Tree Right Side View — BFS level-order traversal; tests understanding of tree width
• Lowest Common Ancestor of a Binary Tree — recursive DFS; tests base-case reasoning
• Number of Islands — BFS/DFS on a grid; the most commonly reported Meta graph problem
• Clone Graph — deep copy with visited tracking; tests reference vs. value understanding
• Word Ladder — BFS shortest path in an implicit graph; tests recognizing when a problem is a graph problem
• Accounts Merge — Union-Find on grouped data; tests whether you reach for the right data structure

Dynamic programming

Expect at least one DP problem in a full Meta loop. The emphasis is on optimization reasoning — can you identify overlapping subproblems and explain why your recurrence works?

• Climbing Stairs — the simplest DP problem; often used as a warm-up or follow-up
• Coin Change — classic unbounded knapsack; tests bottom-up vs. top-down tradeoff discussion
• Longest Increasing Subsequence — O(n log n) patience sorting or O(n²) DP; tests whether you know both
• Word Break — DP with string matching; tests memoization and prefix reasoning
• Decode Ways — string DP with conditional transitions; tests careful state definition

Intervals and sorting

Scheduling and resource allocation problems map directly to infrastructure and product concerns at Meta's scale.

• Merge Intervals — sort + linear merge; the baseline interval problem
• Meeting Rooms II — min-heap or sweep line for concurrent resource counting
• Non-overlapping Intervals — greedy interval scheduling; tests recognizing the greedy choice
• Insert Interval — binary search or linear scan for sorted insertion; tests edge handling

Linked lists and stacks

Linked list manipulation and stack-based parsing are classic signal problems — they test pointer discipline and careful state management.

• Reverse Linked List — iterative and recursive; interviewers often ask for both
• LRU Cache — hash map + doubly linked list; one of the most frequently reported Meta problems
• Valid Parentheses — stack-based matching; a common warm-up
• Flatten a Multilevel Doubly Linked List — recursive or iterative flattening; tests pointer rewiring under complexity
• Add Two Numbers — linked list arithmetic with carry propagation; tests careful traversal

Fresher vs. experienced — what changes at each level

New grad / entry level (E3)

The expectation is clean implementation of medium-difficulty problems. One optimal solution, correct edge-case handling, and a basic complexity analysis. Interviewers want to see that you can write working code under time pressure and explain what you wrote.

Mid level (E4) and senior (E5+)

The problems may not be harder, but the bar for discussion around them is. At E4+, interviewers expect you to compare multiple approaches, discuss tradeoffs proactively — time vs. space, readability vs. performance — and handle follow-up constraints that change the problem mid-conversation. System-level awareness matters: "this works for n = 10⁴, but what if n = 10⁸?" is a question you should be ready for.

Behavioral and design rounds also carry more weight at these levels. Coding alone won't get you through the loop.

How to explain your thought process during the interview

Meta interviewers evaluate communication alongside correctness. A correct solution delivered in silence scores worse than a correct solution delivered with clear reasoning.

A practical framework:

• Clarify before coding. Ask about input size, edge cases, and expected return values. This takes 30 seconds and prevents wrong assumptions that cost 10 minutes.
• State your approach and its tradeoffs out loud. "I'm going to use a hash map for O(1) lookups, which costs O(n) space — is that acceptable?" gives the interviewer a chance to redirect you before you've written 20 lines.
• Narrate as you code. Make your reasoning visible. "I'm initializing the window at index 0 because…" is better than typing in silence.
• Debug collaboratively. When something breaks, name the issue, propose a fix, and confirm with the interviewer. This is how engineers actually work, and it's what interviewers want to see.

For the AI-enabled round specifically: practice narrating while using AI assistance. The interviewer is watching whether you verify the AI's output, catch its mistakes, and explain why a suggestion does or doesn't work. Accepting AI output without inspection is a negative signal.

A practical Meta LeetCode prep plan

Step 1: Filter LeetCode to Facebook-tagged questions, sort by frequency, and work through the top 100 from the last six months. This is the highest-signal subset.

Step 2: If you're starting from scratch or have 3+ months, use LeetCode's Top Interview 150 as a structural backbone to fill pattern gaps.

Step 3: Practice in CoderPad, not just your local IDE. The real interview uses CoderPad, and the environment differences — no autocomplete, no familiar keybindings — can cost you time if you haven't adjusted.

Step 4: Schedule mock interviews at least 10 days before your real interview date. That gives you enough time to identify weak patterns and address them.

Verve AI's Interview Copilot lets you run timed mock coding rounds with real-time feedback — useful for drilling the narration habit before you're in front of a Meta interviewer. The mock interview feature generates structured performance reports after each session, so you can see exactly where your communication or problem-solving breaks down. You can try it free with three sessions.

Step 5: For the AI-enabled round, practice the three-phase workflow separately: find the bug in an unfamiliar codebase, implement a feature using AI suggestions, then optimize. Each phase has a different cognitive demand, and practicing them in isolation builds the muscle memory for switching modes under time pressure.

Behavioral questions alongside the coding rounds

Meta's loop includes a dedicated behavioral round. Coding prep alone is not enough.

Common themes: conflict resolution, failure stories, disagreement with a manager, and "why Meta?" The SPSIL framework — Situation, Problem, Solution, Impact, Learning — gives answers a clear structure that interviewers can follow. Keep behavioral answers specific and concise: name the project, the stakes, the decision, and the outcome.

If you're spending 90% of your prep time on LeetCode and 0% on behavioral stories, you're leaving a full round on the table.

Wrapping up

Meta's coding interviews test patterns, not trivia. Prioritize the six pattern clusters above, filter to recent Meta-tagged questions, and practice narrating your reasoning out loud — especially if you're preparing for the AI-enabled format. The 30 problems in this post are a starting point, not a ceiling. Work through them, identify which patterns feel weakest, and go deeper there.

Verve AI's Mock Interview feature can help you pressure-test your narration and timing before the real thing. Three free sessions, no credit card required.