Rippling mobile engineer interviews focus on iOS or Android platform knowledge, memory management, offline-first architecture, and mobile-specific system design.
1. [AI Enabled Coding] Card Game
Category: String coding problem
Question You are building a simplified card game where each player has a hand of cards and the higher-rated hand wins. Each hand contains exactly...
Input: StringOutput: Computed result
2. [AI Enabled Coding] Design Logger
Category: Array coding problem
Question You need to design a logger library for a new application. The design should be able to allow us to easily add future loggers, like a db...
Input: ArrayOutput: Printed output
3. [AI Enabled Coding] Food Delivery Company
Category: String coding problem
Question You are building a driver payment system for a food delivery company. The accounting team needs to track how much money is owed to drivers...
Input: StringOutput: Integer
4. [AI Enabled Coding] Rule Evaluator
Category: String coding problem
Question You need to build a rule evaluation system for a corporate credit card platform. Managers should be able to create rules that enforce...
Input: ListOutput: Computed result
5. Top 5 Rippling System Design Questions
Category: Sliding window system design problemThese are commonly asked system design questions from Rippling interviewsInput: Given input
Output: Computed result
6. How to pass AI Enabled Coding Rounds From FAANG Interviewer
Category: Algorithm coding problem
Tips For AI Coding Rounds AI coding rounds are not as different from regular coding rounds as you might think. The interviewer still needs to get...
Input: Given inputOutput: Computed result
1. [OA] LRU Cache — implement the caching layer Rippling uses for its API responses
Implement a class
LRUCache with the following methods:LRUCache(int capacity)initializes the LRU cache with a positive sizecapacity.int get(int key)returns the value of the key if the key exists, otherwise returns-1.void put(int key, int value)updates or inserts the value if the key is not already present. When the cache reaches its capacity, it should invalidate the least recently used item before inserting a new item.
Example 1:
Input:
LRUCache cache = new LRUCache(2); cache.put(1, 1); cache.put(2, 2); cache.get(1); // returns 1 Output:
1 Explanation: The cache has the key 1.Example 2:
Input:
cache.put(3, 3); // evicts key 2 Output:
cache.get(2); // returns -1 (not found)Constraints: capacitywill be a positive integer.- The total number of calls to
getandputwill not exceed10^4.
2. [OA] Sliding Window — optimize the user notification system's performance at Rippling
Given a string
notifications representing the incoming notifications and an integer k, return the maximum number of distinct notifications that can be obtained in any sliding window of size k.Example 1: Input:
notifications = "abcabcbb", k = 3 Output:
3 Explanation: The distinct notifications in the window can be
abc.Example 2: Input:
notifications = "aabbcc", k = 2 Output:
2 Explanation: The distinct notifications in the window can be
ab, bc, or ca.Constraints: 1 <= notifications.length <= 10^51 <= k <= 10^5notificationsconsists of lowercase English letters.
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