Most common Twitter coding Interview questions

If you’re preparing for an interview at X (formerly Twitter), you’re likely feeling excitement and nerves. Solid preparation is the key to success, whether you’re aiming for a role in software engineering, product management, or data science. Twitter’s interview process is well-structured and designed to assess technical expertise, system design knowledge, and cultural fit.

This guide will cover each interview category, provide sample questions, and recommend resources to help you crack your Twitter interview. Also, we’ll suggest an AI-powered mock interview platform to refine your skills.

Technical interview questions

Technical interviews at Twitter test your coding skills, problem-solving ability, and algorithmic thinking. Expect questions on data structures, algorithms, and system efficiency. Below are examples of technical questions.

1. Multiply strings

Problem: Given two non-negative integers, num1 and num2, represented as strings, return their product as a string.

You cannot use any built-in BigInteger library. You cannot directly convert the strings to integers (like using int() in Python).

Solution: We multiply two non-negative integers as strings by simulating manual digit-by-digit multiplication. A result array is initialized to store intermediate values, sized to fit the full product. Digits are multiplied from right to left, with sums added to the correct positions and carry-over handled immediately. Finally, the result array is converted to a string, leading zeros are removed, and the product is returned without using built-in integer conversions.

def multiply(num1: str, num2: str) -> str:
    # If either number is "0", the product is "0"
    if num1 == "0" or num2 == "0":
        return "0"

# Initialize the result array with zeros
    # The maximum possible length of the result is len(num1) + len(num2)
    result = [0] * (len(num1) + len(num2))

# Loop through each digit in num1 and num2 from right to left
    for i in range(len(num1) - 1, -1, -1):
        for j in range(len(num2) - 1, -1, -1):
            # Multiply the current digits
            mul = int(num1[i]) * int(num2[j])
            
            # Find the position to add the product in the result array
            sum = mul + result[i + j + 1]
            
            # Update the current position with the single digit
            result[i + j + 1] = sum % 10
            
            # Carry over the remaining value to the next left position
            result[i + j] += sum // 10

# Convert the result array to a string and remove leading zeros
    result_str = ''.join(map(str, result)).lstrip('0')
    
    # Return the final product as a string
    return result_str or "0"

def main():
    
    test_cases = [
        ("2", "3"),
        ("123", "456"),
        ("0", "98765"),
        ("99", "99"),
        ("12345", "6789"),
    ]

for i, (num1, num2) in enumerate(test_cases, 1):
        print("\tnum1:", num1)
        print("\tnum2:", num2)
        result = multiply(num1, num2)
        print("\n\tResult:", result)
        print("-"*100)

if __name__ == "__main__":
    main()

Multiply strings

Time complexity: \( O (n + m) \), where n is the length of num1 and m is the length of num2.

Space complexity: \( m \times n \)

2. Insert delete GetRandom O(1)

Problem: Design a data structure that supports all of the following operations in average \( O(1) \) time:

insert(val): Inserts an item val into the set if not present.
remove(val): Removes an item val from the set if present.
getRandom(): Returns a random element from the current set of elements. Each element must have the same probability of being returned.

Solution: To achieve \( O(1) \) time complexity for insert, remove, and getRandom operations, we can use a combination of a list and a dictionary:

List: Stores the elements, allowing \( O(1) \) access for the getRandom operation.
Dictionary: Maps each element to its index in the list, enabling \( O(1) \) time complexity for insert and remove operations.

import random

class RandomizedSet:
    def __init__(self):
        """
        Initialize the data structure.
        - val_to_index: A dictionary to store the value and its corresponding index in the list.
        - values: A list to store the actual values.
        """
        self.val_to_index = {}  # Maps value to its index in the list
        self.values = []  # Stores the values in an array

def insert(self, val: int) -> bool:
        """
        Inserts a value into the set.
        Returns True if the value was not already present, False otherwise.
        """
        if val in self.val_to_index:
            return False  # Value already exists, so return False
        
        # Store the value's index in the dictionary
        self.val_to_index[val] = len(self.values)
        
        # Append the value to the list
        self.values.append(val)
        
        return True  # Successfully inserted

def remove(self, val: int) -> bool:
        """
        Removes a value from the set.
        Returns True if the value was present, False otherwise.
        """
        if val not in self.val_to_index:
            return False  # Value not found, so return False
        
        # Get the index of the value to be removed
        index = self.val_to_index[val]
        
        # Get the last element in the list
        last_val = self.values[-1]
        
        # Replace the removed element with the last element
        self.values[index] = last_val
        self.val_to_index[last_val] = index  # Update index of last element in the dictionary
        
        # Remove the last element from the list
        self.values.pop()
        
        # Delete the value from the dictionary
        del self.val_to_index[val]
        
        return True  # Successfully removed

def getRandom(self) -> int:
        """
        Returns a random element from the set.
        The function assumes that there is at least one element in the set.
        """
        return random.choice(self.values)  # Select a random element from the list

def main():
    # Create a new RandomizedSet instance
    randomized_set = RandomizedSet()

# Test insertion
    print("Inserting 1:", randomized_set.insert(1))  # True (1 is inserted)
    print("Inserting 2:", randomized_set.insert(2))  # True (2 is inserted)
    print("Inserting 3:", randomized_set.insert(3))  # True (3 is inserted)
    print("Inserting 1 again:", randomized_set.insert(1))  # False (1 is already in the set)

# Test removal
    print("Removing 2:", randomized_set.remove(2))  # True (2 is removed)
    print("Removing 4 (not present):", randomized_set.remove(4))  # False (4 is not in the set)

# Test getRandom multiple times
    print("Random element:", randomized_set.getRandom())  # Should return either 1 or 3
    print("Random element:", randomized_set.getRandom())  # Should return either 1 or 3
    print("Random element:", randomized_set.getRandom())  # Should return either 1 or 3

# Final state of the set
    print("Current elements in set:", randomized_set.values)

if __name__ == "__main__":
    main()

Insert delete GetRandom O(1)

Time complexity: \( O(1) \)

Space complexity: \( O(1) \)

3. Tweet counts per frequency

Problem: Implement the TweetCounts class:

TweetCounts(): Initializes the object.
recordTweet(String tweetName, int time): Stores the tweetName at the recorded time (in seconds).
getTweetCountsPerFrequency(String freq, String tweetName, int startTime, int endTime): Returns an array of the number of tweets for the given tweetName per each time interval. The frequency freq can be “minute,” “hour,” or “day,” representing time intervals of 60 seconds, 3600 seconds, and 86400 seconds respectively.

Solution: We use a dictionary (hash map) to store tweet names as keys and a list of timestamps as values, allowing quick lookups and efficient storage. When recording a tweet, we simply append its timestamp to the list, making it an \( O(1) \) operation. For retrieving tweet counts per frequency, we divide the time range into fixed intervals based on the given frequency—minute (60s), hour (3600s), or day (86400s)—and create a list to store counts for each interval. Using binary search, we efficiently locate timestamps within the given range and count occurrences in each interval.

from collections import defaultdict
import bisect

class TweetCounts:
    def __init__(self):
        """
        Initialize the data structure.
        - tweet_records: A dictionary where the key is the tweet name,
          and the value is a sorted list of timestamps when the tweet was recorded.
        """
        self.tweet_records = defaultdict(list)  # Stores tweet timestamps in a sorted list

def recordTweet(self, tweetName: str, time: int) -> None:
        """
        Records a tweet with its timestamp.
        - Uses bisect.insort() to insert the timestamp while maintaining a sorted order.
        - This ensures O(log N) insertion time complexity.
        """
        bisect.insort(self.tweet_records[tweetName], time)  # Insert while maintaining order

def getTweetCountsPerFrequency(self, freq: str, tweetName: str, startTime: int, endTime: int) -> list[int]:
        """
        Returns the number of tweets in time intervals based on the given frequency.
        - freq: The frequency to aggregate tweet counts ('minute', 'hour', 'day').
        - tweetName: The name of the tweet to count.
        - startTime: The start timestamp.
        - endTime: The end timestamp.

The function divides the time range into chunks based on the frequency
        and counts tweets that fall into each interval using binary search.
        """
        # Mapping frequency to corresponding interval length in seconds
        interval = {'minute': 60, 'hour': 3600, 'day': 86400}[freq]
        result = []  # Stores the tweet count for each interval

# Get the list of timestamps for the given tweet
        times = self.tweet_records[tweetName]

# Iterate through the time range in intervals
        for t in range(startTime, endTime + 1, interval):
            # Define the end of the current interval
            end = min(t + interval, endTime + 1)

# Use binary search to count tweets in the current interval
            count = bisect.bisect_left(times, end) - bisect.bisect_left(times, t)

# Append the count to the result list
            result.append(count)

return result  # Return the list of tweet counts per interval
        
def main():
    # Create an instance of TweetCounts
    tweet_counter = TweetCounts()

# Record some tweets
    print("Recording tweets...")
    tweet_counter.recordTweet("tweet1", 0)
    tweet_counter.recordTweet("tweet1", 10)
    tweet_counter.recordTweet("tweet1", 59)
    tweet_counter.recordTweet("tweet1", 61)
    tweet_counter.recordTweet("tweet1", 120)

tweet_counter.recordTweet("tweet2", 30)
    tweet_counter.recordTweet("tweet2", 150)
    tweet_counter.recordTweet("tweet2", 200)

# Query tweet counts per frequency
    print("\nGetting tweet counts per frequency:")

# Minute-wise frequency count for tweet1
    print("Minute-wise count for 'tweet1' from 0 to 120:", 
          tweet_counter.getTweetCountsPerFrequency("minute", "tweet1", 0, 120))

# Hour-wise frequency count for tweet1
    print("Hour-wise count for 'tweet1' from 0 to 3600:", 
          tweet_counter.getTweetCountsPerFrequency("hour", "tweet1", 0, 3600))

# Day-wise frequency count for tweet1
    print("Day-wise count for 'tweet1' from 0 to 86400:", 
          tweet_counter.getTweetCountsPerFrequency("day", "tweet1", 0, 86400))

# Minute-wise frequency count for tweet2
    print("Minute-wise count for 'tweet2' from 0 to 300:", 
          tweet_counter.getTweetCountsPerFrequency("minute", "tweet2", 0, 300))

# Hour-wise frequency count for tweet2
    print("Hour-wise count for 'tweet2' from 0 to 3600:", 
          tweet_counter.getTweetCountsPerFrequency("hour", "tweet2", 0, 3600))

if __name__ == "__main__":
    main()

Tweet counts per frequency

Time Complexity:

recordTweet: \( O(n) \)
getTweetCountsPerFrequency: \( O(k log n) \)

Space complexity:

recordTweet: \( O(n) \)
getTweetCountsPerFrequency: \( O(k) \)

4. Design Twitter

Problem: Design a simplified version of Twitter where users can post tweets, follow/unfollow another user, and see the 10 most recent tweets in the user’s news feed. Implement the Twitter class:

postTweet(int userId, int tweetId): Compose a new tweet.
getNewsFeed(int userId): Retrieve the 10 most recent tweet IDs in the user’s news feed. Each item in the news feed must be posted by users who the user followed or by the user themselves. Tweets must be ordered from most recent to least recent.
follow(int followerId, int followeeId): Follower follows a followee.
unfollow(int followerId, int followeeId): Follower unfollows a followee.

Solution: We use a dictionary (hashmap) to store the following relationships, mapping each user to a set of users they follow, ensuring efficient lookups for feed generation. We maintain a global timestamp for tweets to track tweet order and store tweets in a dictionary where each user maps to their list of posted tweets. When retrieving the news feed, we gather tweets from the user and their followers, then use a min heap to efficiently retrieve and sort the 10 most recent tweets in descending order.

import heapq
import itertools
from collections import defaultdict, deque

class Twitter:
    def __init__(self):
        """
        Initialize the Twitter data structure.
        - timestamp: A counter that provides decreasing timestamps to maintain tweet order.
        - tweets: A dictionary mapping userId to a deque containing their latest tweets.
        - followees: A dictionary mapping userId to a set of users they follow.
        """
        self.timestamp = itertools.count(step=-1)  # Decreasing timestamps to maintain tweet order
        self.tweets = defaultdict(deque)  # Maps userId -> deque of (timestamp, tweetId)
        self.followees = defaultdict(set)  # Maps userId -> set of followeeIds

def postTweet(self, userId: int, tweetId: int) -> None:
        """
        A user posts a tweet.
        - The tweet is stored with a unique decreasing timestamp for ordering.
        - Uses a deque (double-ended queue) to store up to 10 recent tweets per user.
        """
        self.tweets[userId].appendleft((next(self.timestamp), tweetId))  # Add tweet at the front
        if len(self.tweets[userId]) > 10:  # Keep only the latest 10 tweets
            self.tweets[userId].pop()

def getNewsFeed(self, userId: int) -> list[int]:
        """
        Retrieve the 10 most recent tweets from the user and their followees.
        - Uses a min-heap to keep track of the latest 10 tweets.
        - Tweets are sorted in descending order based on timestamps.
        """
        min_heap = []  # Min-heap to store the top 10 tweets
        users = self.followees[userId] | {userId}  # Get all followees + the user themselves

for user in users:
            for tweet in self.tweets[user]:  # Iterate through the user's tweets
                heapq.heappush(min_heap, tweet)  # Add tweet to min-heap
                if len(min_heap) > 10:  # Keep only the top 10 most recent tweets
                    heapq.heappop(min_heap)

# Extract tweet IDs from the heap and sort them in descending order (latest first)
        return [tweetId for _, tweetId in sorted(min_heap, reverse=True)]

def follow(self, followerId: int, followeeId: int) -> None:
        """
        Follows a user.
        - Adds followeeId to the set of users that followerId follows.
        - A user cannot follow themselves.
        """
        if followerId != followeeId:
            self.followees[followerId].add(followeeId)

def unfollow(self, followerId: int, followeeId: int) -> None:
        """
        Unfollows a user.
        - Removes followeeId from followerId's followees set.
        - If the followee is not in the set, this operation does nothing.
        """
        self.followees[followerId].discard(followeeId)  # Remove followee if exists
        
def main():
    # Create a Twitter instance
    twitter = Twitter()

# User 1 posts tweets
    print("User 1 posts tweet 101")
    twitter.postTweet(1, 101)
    
    print("User 1 posts tweet 102")
    twitter.postTweet(1, 102)

# User 1's news feed should return [102, 101]
    print("User 1's News Feed:", twitter.getNewsFeed(1))

# User 2 follows User 1
    print("User 2 follows User 1")
    twitter.follow(2, 1)

# User 2 posts a tweet
    print("User 2 posts tweet 201")
    twitter.postTweet(2, 201)

# User 2's news feed should return [201] since they only see their own tweets
    print("User 2's News Feed:", twitter.getNewsFeed(2))

# User 1 posts another tweet
    print("User 1 posts tweet 103")
    twitter.postTweet(1, 103)

# User 2's news feed should now include User 1's tweets because they follow User 1
    print("User 2's News Feed after following User 1:", twitter.getNewsFeed(2))

# User 2 unfollows User 1
    print("User 2 unfollows User 1")
    twitter.unfollow(2, 1)

# User 2's news feed should now only contain their own tweet again
    print("User 2's News Feed after unfollowing User 1:", twitter.getNewsFeed(2))

if __name__ == "__main__":
    main()

Design Twitter

5. Best meeting point

Problem: Given a 2D grid of values 0 or 1, where each 1 marks the home of someone in a group, find the minimal total travel distance to a meeting point. The total travel distance is the sum of the distances between the group members’ homes and the meeting point.

Solution: We use the mathematical property that the median minimizes the sum of absolute deviations. First, we collect the row and column indexes of all 1s (homes) in the grid separately. As the optimal meeting point lies near the median, we find the median row and median column from these collected indexes. Finally, we compute the total Manhattan distance, the sum of absolute differences between each home’s and median coordinates.

def minTotalDistance(grid: list[list[int]]) -> int:
    # Collect row indexes of all homes (1s) in the grid
    rows = [i for i, row in enumerate(grid) for val in row if val == 1]

# Collect column indexes of all homes (1s) in the grid
    cols = [j for row in grid for j, val in enumerate(row) if val == 1]

# Find the median row
    median_row = rows[len(rows) // 2]

# Find the median column
    median_col = cols[len(cols) // 2]

# Compute the total Manhattan distance (sum of absolute differences)
    total_distance = sum(abs(r - median_row) for r in rows) + sum(abs(c - median_col) for c in cols)

return total_distance

def main():
    # Define test cases
    test_cases = [
        [[1, 0, 0, 0, 1], [0, 0, 0, 0, 0], [0, 0, 1, 0, 0]],
        [[1, 1], [1, 1]],
        [[0, 1, 0], [0, 1, 0], [0, 1, 0]],
        [[1, 0, 1], [0, 0, 0], [1, 0, 1]],
    ]

# Run test cases
    for grid in test_cases:
        print("Grid:", grid)
        result = minTotalDistance(grid)
        print("Result:", result)
        print("-"*70)

if __name__ == "__main__":
    main()

Best meeting point

Time complexity: \( O(m * n ) \), where m is the number of rows and n is the number of columns.

Space complexity: \( O(k) \), where k is the number of 1s in the grid.

Additional problems

1. Flatten Nested List Iterator

Problem: You are given a nested list of integers NestedInteger. Each element is either an integer or a list whose elements may also be integers or other lists. Implement an iterator to flatten it.

Implement the NestedIterator class:

NestedIterator(List<NestedInteger> nestedList): Initializes the iterator with the nested list.
next(): Returns the next integer in the nested list.
hasNext(): Returns true if some integers are still in the nested list and false otherwise.

2. Kth largest element in an array

Problem: Given an unsorted array of integers, find the kth largest element. You may use algorithms like QuickSelect for average-case O(n) time or a Min Heap of size k for O(n log k) time.

3. Cycle detection in a linked list

Problem: Given a linked list, determine if it contains a cycle. Implement the solution using Floyd’s Tortoise and Hare algorithm, which uses two pointers moving at different speeds to detect cycles with O(n) time and O(1) space.

4. Merge K sorted linked lists

Problem: Given an array of k sorted linked lists, merge them into a single sorted linked list. Achieve an efficient solution using a priority queue (min-heap) to always select the smallest current node from the lists, with a time complexity of O(N log k), where N is the total number of nodes.

5. LRU (Least recently used) Cache

Problem: Design and implement an LRU Cache with a fixed capacity that supports get() and put() operations in O(1) time. Use a combination of a HashMap for quick lookups and a Doubly Linked List to keep track of the usage order of elements.

6. Longest substring without repeating characters

Problem: Given a string, find the length of the longest substring that contains no repeating characters. Use the Sliding Window technique with a HashSet or HashMap to achieve an optimal solution with O(n) time complexity.

7. URL shortener design

Problem: Design a URL shortener system that converts long URLs into shorter ones and allows redirecting from the short URL to the original. Use Base62 encoding (including uppercase and lowercase letters and digits) and a hashing mechanism to generate unique codes while handling collisions and ensuring scalability.

System Design interview questions

Twitter’s system design interviews assess your ability to design scalable, efficient, and distributed systems. Expect questions on architecture, trade-offs, and best practices.

Below are examples of system design questions.

1. Design Twitter’s tweet storage and retrieval system

Problem: How would you design a system that efficiently stores and retrieves tweets for millions of users in real time?

Key features:

Efficient storage of tweets
Fast retrieval of tweets for a given user
Handling trending tweets

Design highlights:

Use NoSQL databases like Cassandra for high-write scalability.
Implement sharding and replication to handle large data loads.
Use Redis or Memcached for fast retrieval of recent tweets.

2. Design a rate limiter for Twitter API

Problem: How would you design a rate limiting system to prevent excessive API requests?

Key features:

Prevent API abuse and DoS attacks
Allow controlled access to APIs
Implement rate limits per user, IP, or application

Design highlights:

Use a Token Bucket Algorithm or Leaky Bucket Algorithm.
Store rate limits in a distributed cache (Redis) for fast access.
Implement exponential backoff for repeated violations.

3. Design a Twitter search engine

Problem: How would you design a search system to handle billions of tweets and provide fast results?

Key features:

Full-text search across tweets
Real-time updates to indexed data
Ranking tweets based on relevance

Design highlights:

Use Elasticsearch for full-text search.
Implement inverted indexes for efficient querying.
Use sharding and partitioning to scale horizontally.

4. Design a Load Balancer

Problem: Design a system to efficiently distribute incoming network traffic across multiple servers.

Key features:

Balance traffic based on requests per second (RPS)
Perform regular health checks to identify and exclude failed servers
Enable horizontal scaling to handle increased traffic

Design highlights:

Utilize load balancing strategies like Round Robin or Least Connections.
Deploy tools such as NGINX, HAProxy, or cloud-based load balancers.
Apply consistent hashing to achieve more balanced and reliable traffic distribution.

Behavioral interview questions

Behavioral interviews at Twitter focus on problem-solving, collaboration, and leadership skills. Expect situational and experience-based questions. Below are examples of behavioral questions.

Q1. Tell me about a time you solved a complex problem.

Example answer: While working on a project, I noticed a high API latency affecting thousands of users. I analyzed the logs, found inefficient database queries, and optimized them using indexing and caching. This reduced response time by 60% and significantly improved user experience.

Q2. Describe a situation where you handled a conflict within a team.

Example answer: During a product discussion, a teammate and I disagreed on implementing a caching strategy. To resolve the conflict, we held a meeting, reviewed data from a small-scale test, and made an informed decision. This collaborative approach strengthened our team dynamics and improved system efficiency.

Q3. Tell me about a time when you led a project under tight deadlines.

Example answer: During a Twitter feature rollout, unexpected database issues arose. I quickly coordinated with engineers, implemented auto-scaling, and resolved the issue in 24 hours, ensuring a successful, on-time launch.

Q4. Give an example of when you had to convince others to adopt your idea.

Example answer: I proposed using GraphQL instead of REST APIs for an internal service, but my team was hesitant. I demonstrated the benefits through a small proof of concept, showing reduced API calls and improved efficiency. This convinced the team to adopt GraphQL.

Pro tip: Structure your responses using the STAR method (Situation, Task, Action, Result).

Q5. Describe how you handle feedback and criticism.

Example answer: During a code review at Twitter, a senior engineer pointed out that my feature implementation wasn’t scalable for future growth. Instead of taking it personally, I scheduled a quick follow-up to better understand the concerns. After the discussion, I refactored the code, applying a more modular design pattern. Not only did this make the feature scalable, but it also improved my coding practices. I appreciate constructive feedback because it helps me grow and deliver better solutions.

Q6. How do you ensure inclusivity and diversity in your work?

Example answer: In a previous project, I noticed that a few voices dominated team meetings while others, particularly remote teammates, rarely contributed. To address this, I suggested implementing structured agendas with dedicated time for everyone to share updates. I also encouraged asynchronous feedback through shared documents. This created a more inclusive environment where all perspectives were heard, leading to stronger solutions and higher team morale. I believe diverse input leads to better, more user-centered products.

General tips to ace your Twitter interview

Know Twitter’s products and features: Stay updated on Twitter Blue, Spaces, Communities, and X’s future vision.
Understand Twitter’s business model: How does Twitter make money? What are its revenue streams?
Practice behavioral questions: Use STAR format and demonstrate Twitter’s values.
Prepare for technical rounds: For engineers, focus on data structures, algorithms, and system design.
Follow Twitter trends: Be aware of recent news, platform updates, and controversies.
Ask thoughtful questions: Show your enthusiasm by asking about team culture, challenges, and company goals.

Refine your skills with AI-powered mock interviews

Increase your chances of success with AI-driven mock interview platforms. These tools offer realistic coding, system design, and behavioral simulations, providing instant feedback to help you improve and refine your performance.

Recommended resources

Grokking the Coding Interview Patterns: Learn 26 essential coding patterns to easily tackle thousands of LeetCode-style questions. Prepare efficiently for coding interviews with the ultimate course, designed by FAANG engineers.
Grokking the Modern System Design Interview: The ultimate guide to System Design Interviews, crafted by FAANG engineers. Master distributed systems fundamentals and sharpen your skills with real-world interview questions and mock interviews.
Grokking the Behavioral Interview: Whether you’re a software engineer, product manager, or engineering manager, this course equips you with the tools to confidently tackle behavioral and cultural questions. Beyond technical roles, it offers valuable insights for professionals in any field.
Grokking the Low-Level Design Interview Using OOD Principles: A battle-tested guide to Object-Oriented Design (OOD) Interviews, crafted by FAANG engineers. Master OOD fundamentals and sharpen your skills with real-world interview questions.
Grokking the Product Architecture Design Interview: The essential guide to API Design & Product Design Interviews – developed by FAANG engineers. Master product design fundamentals & get hands-on with real-world APIs.

Conclusion

Twitter interviews demand solid preparation in coding, system design, and behavioral alignment with the company’s fast-paced culture. Focus on data structures, algorithms, and real-world scalability challenges to excel in the technical rounds. System design expertise is crucial for mid-to-senior roles, so practice designing scalable architectures. Behavioral questions assess your collaboration, leadership, and adaptability, so prepare structured responses. Use LeetCode, Educative courses, and mock interviews to sharpen your problem-solving skills. With consistent practice and confidence, you’ll be well-prepared to land your dream job at Twitter!

Best of luck!

Frequently Asked Questions

What programming languages are preferred for Twitter interviews?

Twitter doesn’t mandate a specific language, but Python, Java, C++, and Scala are commonly used. Choose a language you’re most comfortable with, as clarity and efficiency matter more than the language itself.

Does Twitter ask system design questions for junior roles?

System design is more common for mid-level and senior roles, but juniors may get simplified questions. They might ask about designing a basic feature (e.g., a tweet storage system) rather than a full-scale distributed architecture.

How much does culture fit matter in Twitter interviews?

A lot! Twitter values inclusivity, innovation, and open communication. Expect behavioral questions that assess your alignment with their culture, such as how you collaborate, handle feedback, or contribute to a diverse team.

How long does the Twitter interview process take?

Depending on the role and team, it typically takes 3–6 weeks. The process includes an initial recruiter screen, technical rounds (coding + system design), and a final onsite with behavioral and leadership interviews.

How competitive is the Twitter interview process?

Twitter’s interviews are challenging but not impossible. They assess problem-solving skills, coding efficiency, system design expertise, and cultural fit. Strong preparation in algorithms, real-world architecture, and communication will give you an edge.

Company Interview Questions

Most common Twitter coding Interview questions

Technical interview questions

1. Multiply strings

2. Insert delete GetRandom O(1)

3. Tweet counts per frequency

4. Design Twitter

5. Best meeting point

Additional problems

1. Flatten Nested List Iterator

2. Kth largest element in an array

3. Cycle detection in a linked list

4. Merge K sorted linked lists

5. LRU (Least recently used) Cache

6. Longest substring without repeating characters

7. URL shortener design

System Design interview questions

1. Design Twitter’s tweet storage and retrieval system

2. Design a rate limiter for Twitter API

3. Design a Twitter search engine

4. Design a Load Balancer

Behavioral interview questions

General tips to ace your Twitter interview

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Conclusion

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