How to Crack Meta Coding Interviews in 2026
Complete guide to Meta coding interviews — question patterns, difficulty breakdown, must-practice topics, and preparation strategy.
Meta (formerly Facebook) runs one of the most coding-heavy interview processes in big tech. The typical loop includes an initial recruiter screen, one or two technical phone screens, and then an on-site consisting of two coding rounds, one system design round (for E4+), and one behavioral round. What distinguishes Meta is the pace — each coding round is 35 to 40 minutes, and you are expected to solve two medium-level problems or one hard problem in that window. This is faster than most other companies, and it means speed matters. If you cannot get to working code quickly, you will struggle regardless of how well you understand the concepts.
Meta interviewers tend to be direct. They want to see you write correct, efficient code with minimal hand-holding. The problems lean toward well-known patterns but with enough variation to test whether you truly understand the underlying concepts or have simply memorized solutions.
By the Numbers
Meta's question pool contains 1,387 questions, making it the third largest among the major tech companies. The difficulty breakdown is notably friendlier than Google's:
- Easy: 414 questions (30%)
- Medium: 762 questions (55%)
- Hard: 211 questions (15%)
The 30% easy rate is the highest among FAANG companies, and the 15% hard rate is the lowest. This does not mean Meta interviews are easy — it means the difficulty curve is shifted toward the medium range. Expect to face two medium problems per round rather than one hard. The challenge is speed and accuracy, not raw difficulty.
Top Topics to Focus On
Arrays — Array manipulation is the bread and butter of Meta interviews. Two pointers, sliding window, prefix sums, and in-place modifications appear constantly. Many candidates report that at least one of their interview problems was array-based.
Let's look at a classic two-pointer problem: Two Sum II - Input Array Is Sorted. The goal is to find two numbers in a sorted array that add up to a target. The two-pointer technique is optimal here.
def two_sum_sorted(numbers, target):
left, right = 0, len(numbers) - 1
while left < right:
current_sum = numbers[left] + numbers[right]
if current_sum == target:
return [left + 1, right + 1] # 1-indexed
elif current_sum < target:
left += 1
else:
right -= 1
return [-1, -1] # Not found
Strings — Meta has a particular fondness for string problems. Valid palindrome variations, substring searches, and string transformation problems are common. Pay special attention to problems involving two-pointer techniques applied to strings.
A frequent variation is checking if a string is a palindrome after removing at most one character. This requires careful two-pointer logic.
def valid_palindrome_ii(s):
def is_palindrome_range(left, right):
while left < right:
if s[left] != s[right]:
return False
left += 1
right -= 1
return True
left, right = 0, len(s) - 1
while left < right:
if s[left] != s[right]:
# Try skipping either the left or right character
return (is_palindrome_range(left + 1, right) or
is_palindrome_range(left, right - 1))
left += 1
right -= 1
return True
Hash Tables — Frequency counting, grouping, and lookup problems are everywhere in Meta's question bank. Hash maps are often the key to converting a brute force O(n^2) solution into an optimal O(n) one, and Meta interviewers expect you to make that leap quickly.
A quintessential problem is Group Anagrams, where you group strings that are anagrams of each other.
def group_anagrams(strs):
from collections import defaultdict
anagram_map = defaultdict(list)
for s in strs:
# Use sorted string as key
key = ''.join(sorted(s))
anagram_map[key].append(s)
return list(anagram_map.values())
Math — Meta asks more math-flavored problems than you might expect. Problems involving arithmetic operations, number properties, and basic combinatorics appear regularly. These tend to be on the easier side individually, but they can trip you up if you have not practiced them.
A common example is Reverse Integer, which tests your understanding of integer overflow and modulo arithmetic.
def reverse_integer(x):
INT_MAX, INT_MIN = 2**31 - 1, -2**31
rev = 0
sign = 1 if x >= 0 else -1
x = abs(x)
while x != 0:
pop = x % 10
x //= 10
# Check for overflow before multiplying
if rev > (INT_MAX - pop) // 10:
return 0
rev = rev * 10 + pop
return sign * rev
Dynamic Programming — While DP is not as dominant at Meta as it is at Google, it still appears in roughly 10-15% of interview questions. Focus on the most common patterns: 1D DP, string-based DP (edit distance, longest common subsequence), and simple 2D grid problems.
A foundational 1D DP problem is Climbing Stairs, which is essentially Fibonacci.
def climb_stairs(n):
if n <= 2:
return n
dp = [0] * (n + 1)
dp[1], dp[2] = 1, 2
for i in range(3, n + 1):
dp[i] = dp[i - 1] + dp[i - 2]
return dp[n]
# Space-optimized version
def climb_stairs_opt(n):
if n <= 2:
return n
first, second = 1, 2
for _ in range(3, n + 1):
third = first + second
first, second = second, third
return second
Preparation Strategy
Weeks 1-2: Speed Drills on Core Topics. Meta rewards speed. Start by solving 8-10 easy and medium problems per day across arrays, strings, and hash tables. Use a strict timer: 15 minutes per easy, 25 minutes per medium. If you cannot solve it in time, look at the solution, understand it deeply, and re-solve it the next day without help.
Week 3: Graph and Tree Mastery. Binary tree problems (level-order traversal, serialize/deserialize, path sums) and graph problems (BFS, DFS, number of islands, clone graph) are Meta staples. Spend this week doing 5-6 problems per day from these categories. Focus on writing clean recursive and iterative solutions for tree problems.
Let's examine a classic graph problem: Number of Islands using BFS/DFS.
def num_islands(grid):
if not grid:
return 0
rows, cols = len(grid), len(grid[0])
count = 0
def dfs(r, c):
if r < 0 or c < 0 or r >= rows or c >= cols or grid[r][c] != '1':
return
grid[r][c] = '0' # Mark as visited
dfs(r + 1, c)
dfs(r - 1, c)
dfs(r, c + 1)
dfs(r, c - 1)
for r in range(rows):
for c in range(cols):
if grid[r][c] == '1':
count += 1
dfs(r, c)
return count
Week 4: Dynamic Programming and Backtracking. Cover the essential DP patterns and backtracking problems (subsets, permutations, combination sum). Meta does not go as deep into DP as Google, so you can focus on the 20 most common DP problems rather than trying to cover everything.
A standard backtracking problem is Subsets, generating all possible subsets of a set.
def subsets(nums):
result = []
def backtrack(start, current):
result.append(current[:])
for i in range(start, len(nums)):
current.append(nums[i])
backtrack(i + 1, current)
current.pop()
backtrack(0, [])
return result
Week 5: Two-Problem Practice and Mock Interviews. Start simulating real Meta rounds: set a 40-minute timer and solve two medium problems back to back. This is the single most important exercise for Meta preparation. Do this at least twice a day. Supplement with 2-3 full mock interviews.
Week 6: Review, Weak Spots, and Behavioral Prep. Revisit every problem you failed or took too long on. Identify your weak patterns and drill them. Meta's behavioral round focuses on collaboration, conflict resolution, and impact — prepare 5-6 stories. Also review system design fundamentals if you are interviewing for E4 or above.
Key Tips
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Speed is non-negotiable. Meta expects two problems in 35-40 minutes. If you are not finishing problems within 15-20 minutes during practice, you are not ready. Prioritize speed once your accuracy is solid.
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Know your BFS and DFS cold. Graph traversal problems are among Meta's most frequently asked. You should be able to write BFS and DFS in your sleep, including variations like multi-source BFS and cycle detection.
Here is a template for iterative BFS on a graph represented as an adjacency list:
from collections import deque
def bfs(graph, start):
visited = set([start])
queue = deque([start])
while queue:
node = queue.popleft()
# Process node
print(node)
for neighbor in graph[node]:
if neighbor not in visited:
visited.add(neighbor)
queue.append(neighbor)
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Communicate concisely. Meta interviewers appreciate brevity. State your approach in 2-3 sentences, confirm with the interviewer, then start coding. Long-winded explanations eat into your limited time.
-
Handle edge cases proactively. Before writing code, call out the key edge cases (empty input, single element, duplicates). This shows maturity and saves you from debugging later.
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Practice on a plain text editor. Meta's coding environment is minimal — no autocomplete, no syntax highlighting in some cases. Get comfortable writing code without IDE assistance.