How to Crack LinkedIn Coding Interviews in 2026
Complete guide to LinkedIn coding interviews — question patterns, difficulty breakdown, must-practice topics, and preparation strategy.
LinkedIn, now under the Microsoft umbrella, maintains its own distinct engineering culture and interview process. The platform serves over 900 million professionals, and the engineering challenges — real-time feed ranking, graph-based recommendations, messaging at scale — are reflected in the types of problems interviewers ask. LinkedIn coding interviews are rigorous and lean toward graph and search-based thinking, which makes sense given the company's core product is essentially a massive social graph.
The interview process typically includes a recruiter screen, a phone screen with one coding problem, and a virtual on-site consisting of four rounds: two coding, one system design (for senior+), and one behavioral. LinkedIn places a strong emphasis on "InDay" culture and collaboration, so expect behavioral questions to probe how you work with cross-functional teams.
By the Numbers
LinkedIn's CodeJeet question bank has 180 questions. The difficulty distribution is demanding:
- Easy: 26 questions (14%) — A thin layer. LinkedIn does not spend much interview time on easy problems.
- Medium: 117 questions (65%) — Two-thirds of the pool. This is where you will spend most of your time, both in prep and in the actual interview.
- Hard: 37 questions (21%) — One in five questions is Hard. Senior candidates should expect at least one Hard-level question in their loop.
The 14/65/21 split is characteristic of a company that expects strong algorithmic fundamentals. If you are targeting LinkedIn, you need to be very comfortable solving Medium problems quickly and have a strategy for approaching Hards.
Top Topics to Focus On
Array — The most common topic across all difficulty levels. Expect subarray problems, prefix sums, sliding windows, and array-based simulation problems. LinkedIn array questions tend to be well-defined but require careful implementation. For example, a classic problem is finding the maximum sum of a contiguous subarray (Kadane's Algorithm) or finding all subarrays that sum to a target value using a prefix sum hash map.
String — String manipulation, parsing, and pattern matching. LinkedIn's string problems often involve real-world scenarios like text processing, URL parsing, or search query handling, which aligns with the platform's product surface. Common patterns include checking for palindromes, string compression, and implementing basic parsers.
Hash Table — Critical for optimizing solutions. LinkedIn interviewers expect you to reach for hash maps when appropriate, and they will push back if you present an O(n^2) solution that has an obvious O(n) hash-based alternative. Hash tables are frequently used for frequency counting, memoization, and storing previously seen elements to achieve constant-time lookups.
Depth-First Search — This is LinkedIn's signature topic. Given that the platform is fundamentally a graph of professional connections, DFS and graph traversal problems appear more frequently here than at most other companies. Practice DFS on trees, graphs, and matrices. Know how to detect cycles, find connected components, and traverse in multiple directions. You must be able to implement both recursive and iterative (using a stack) versions.
Dynamic Programming — With 21% Hard questions, DP shows up regularly at the senior level. Focus on sequence-based DP, string DP, and decision-making problems. LinkedIn DP questions are rarely straightforward — they often require you to define the state carefully. Classic problems include the Longest Increasing Subsequence, Edit Distance, and various knapsack problems.
Preparation Strategy
Weeks 1-2: Fundamentals and Graph Basics
Spend the first week on Easy and low-Medium problems in arrays, strings, and hash tables. In week two, shift focus to graph fundamentals: adjacency list representation, BFS, DFS, and topological sort. Solve 3 to 4 problems per day. Since LinkedIn emphasizes DFS, build a strong mental model of recursive traversal early.
Let's look at a fundamental DFS implementation for traversing an undirected graph represented as an adjacency list.
def dfs_iterative(graph, start):
"""Iterative DFS using a stack."""
visited = set()
stack = [start]
traversal_order = []
while stack:
node = stack.pop()
if node not in visited:
visited.add(node)
traversal_order.append(node)
# Add neighbors to stack. Extend in reverse for consistent order.
for neighbor in reversed(graph[node]):
if neighbor not in visited:
stack.append(neighbor)
return traversal_order
def dfs_recursive(graph, node, visited=None, traversal_order=None):
"""Recursive DFS."""
if visited is None:
visited = set()
if traversal_order is None:
traversal_order = []
if node in visited:
return traversal_order
visited.add(node)
traversal_order.append(node)
for neighbor in graph[node]:
dfs_recursive(graph, neighbor, visited, traversal_order)
return traversal_order
# Example usage
if __name__ == "__main__":
# Graph: 0 - 1 - 2
# \ /
# 3
graph = {
0: [1, 3],
1: [0, 2, 3],
2: [1, 3],
3: [0, 1, 2]
}
print("Iterative DFS:", dfs_iterative(graph, 0))
print("Recursive DFS:", dfs_recursive(graph, 0))
Weeks 3-4: Medium Problems and DFS Depth
Dedicate these two weeks to Medium-difficulty problems. Alternate between array/string problems and graph/DFS problems. For DFS, practice on trees (path sum, diameter, lowest common ancestor) and graphs (number of islands, course schedule, clone graph). Aim for 2 to 3 problems per day under a 30-minute timer.
A quintessential Medium problem is "Number of Islands," which uses DFS on a grid. This pattern is directly applicable to LinkedIn's graph-based data.
def num_islands(grid):
"""Count the number of islands using DFS."""
if not grid:
return 0
rows, cols = len(grid), len(grid[0])
count = 0
def dfs(r, c):
# Base cases: out of bounds or not land
if r < 0 or r >= rows or c < 0 or c >= cols or grid[r][c] != '1':
return
# Mark as visited by setting to '0'
grid[r][c] = '0'
# Explore all four directions
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
# Example usage
if __name__ == "__main__":
grid = [
["1", "1", "0", "0", "0"],
["1", "1", "0", "0", "0"],
["0", "0", "1", "0", "0"],
["0", "0", "0", "1", "1"]
]
print("Number of islands:", num_islands(grid)) # Output: 3
Week 5: Hard Problems and System Design
Tackle 8 to 10 Hard problems with a focus on DP and graph-based questions. For system design, study LinkedIn-relevant systems: design a social feed, a connection recommendation engine, a messaging system, or a notification service. Understand how LinkedIn handles their social graph at scale — their engineering blog is an excellent resource.
A classic Hard DP problem is the "Longest Increasing Subsequence" (LIS). This demonstrates the need to carefully define the DP state and transition.
def length_of_lis(nums):
"""Find the length of the longest strictly increasing subsequence using DP."""
if not nums:
return 0
n = len(nums)
# dp[i] represents the length of the LIS ending at index i
dp = [1] * n
for i in range(n):
for j in range(i):
if nums[i] > nums[j]:
dp[i] = max(dp[i], dp[j] + 1)
return max(dp)
# Optimized O(n log n) version using patience sorting
import bisect
def length_of_lis_optimized(nums):
tails = []
for num in nums:
# Find the position to replace or append
idx = bisect.bisect_left(tails, num)
if idx == len(tails):
tails.append(num)
else:
tails[idx] = num
return len(tails)
# Example usage
if __name__ == "__main__":
nums = [10, 9, 2, 5, 3, 7, 101, 18]
print("DP O(n^2) LIS length:", length_of_lis(nums)) # Output: 4
print("Optimized O(n log n) LIS length:", length_of_lis_optimized(nums)) # Output: 4
Week 6: Mock Interviews and Refinement
Conduct three to four mock interviews. Structure each with one Medium and one Medium-to-Hard problem over 50 minutes. Practice thinking aloud — LinkedIn interviewers evaluate your communication as much as your code. Revisit any DFS or DP problems you struggled with and make sure you can solve them cleanly.
Key Tips
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Master DFS and graph traversal. This is non-negotiable for LinkedIn. If you cannot confidently write DFS from scratch — both recursive and iterative versions — you are leaving points on the table. Practice on trees and graphs until traversal is automatic. Understand the trade-offs: recursion is elegant but risks stack overflow on deep graphs; iteration with an explicit stack gives you more control.
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Think about the product. LinkedIn interviewers appreciate candidates who can connect algorithmic problems to real-world product scenarios. When solving a graph problem, it is worth noting how it relates to connection recommendations or feed ranking. For example, a problem about finding the shortest path in an unweighted graph (BFS) could be related to finding the degree of connection between two members.
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Handle follow-ups gracefully. LinkedIn interviewers often extend the initial problem with a twist: "Now what if the graph has cycles?" or "What if we need the k shortest paths?" Practice thinking about generalizations while you solve the base problem. For instance, after solving "Number of Islands," a common follow-up is "What if the grid is too large to fit in memory?" This tests your ability to think about distributed processing or streaming algorithms.
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Prepare for the behavioral round seriously. LinkedIn's culture emphasizes collaboration, integrity, and member-first thinking. Prepare specific stories about teamwork, conflict resolution, and times you prioritized the user experience over technical convenience. Use the STAR method (Situation, Task, Action, Result) to structure your answers.
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Study LinkedIn's engineering blog. The company publishes detailed technical posts about their infrastructure, data pipelines, and ML systems. Familiarity with their tech stack (Kafka, Samza, Venice, Espresso) shows genuine interest and helps in system design rounds. For example, knowing that they use Kafka for real-time data streaming can inform your design for a notification system.