DoorDash software engineer interviews cover algorithms, data structures, system design, and coding problems drawn from real interview rounds.
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coding
Hard
Verified Question
#1
1. Code Craft - Driver Payment System
Category: Algorithm coding problem
Question You are in charge of implementing the Dasher payment model. Given the sequence of accepted/fulfilled order activities from a given dasher...
Input: ListOutput: Computed result
coding
Hard
Verified Question
#2
2. Wildcard Segment Counter
Category: String coding problemYou are given a
template string consisting only of the characters '0', '1', and '?', and a list of integers run_lengths. A '?' in the...Input: Array of integersOutput: Computed result
coding
Hard
Verified Question
#3
3. Directory Registry
Category: Tree coding problemImplement a
DirectoryRegistry class that manages a hierarchical key-value store modeled as a tree of paths. The root path "/" always exists with...Input: StringOutput: Computed result
coding
Hard
Verified Question
#4
4. Ride Earnings Calculator
Category: String coding problemYou are given
records, a list of ride events. Each record is a list of three strings: [ride_id, timestamp, status]. Possible statuses are...Input: ListOutput: Computed result
system design
Hard
Verified Question
#5
5. Top 8 Doordash System Design Questions Jan 2026
Category: Linked list system design problem
System Design Questions - DoorDash These are the commonly asked system design questions from DoorDash interviews. Updated January 2026.
Input: Linked listOutput: Computed result
coding
Hard
graph
#1
1. [OA] Depth-First Search — Optimize Driver Matching for DoorDash
DoorDash needs to effectively match drivers to delivery requests based on location and time constraints.
Problem Statement: Given a grid of
Input:
Output:
Explanation: The only reachable order is at (1,1).Constraints:
Problem Statement: Given a grid of
n x m representing a map where 1 represents an order location and 0 represents an empty space, implement a function maxDrivers(orders: List[List[int]], start: Tuple[int, int]) -> int that returns the maximum number of deliveries that can be assigned to drivers originating from start position.Example 1:Input:
orders = [[0,0,0],[0,1,0],[0,0,0]], start = (1, 1) Output:
1 Explanation: The only reachable order is at (1,1).Constraints:
1 <= orders.length, orders[i].length <= 200 <= start[0] < orders.length0 <= start[1] < orders[i].length
coding
Hard
sliding window
#2
2. [OA] Sliding Window — Design a delivery tracking system for DoorDash
DoorDash needs a robust method to track orders and ensure timely deliveries based on real-time traffic data.
Problem Statement: Given an array of integers representing the estimated delivery times for incoming orders, implement a function
Input:
Output:
Explanation: The best sub-array is
Problem Statement: Given an array of integers representing the estimated delivery times for incoming orders, implement a function
maxDeliveryTime(orders: List[int], k: int) -> int that returns the maximum possible delivery time over any sub-array of size k, where k is the number of concurrent deliveries.Example 1:Input:
orders = [2, 1, 3, 5, 6, 4], k = 3 Output:
14 Explanation: The best sub-array is
[5, 6, 4], which yields a delivery time of 5 + 6 + 4 = 15.Constraints: 1 <= orders.length <= 1000001 <= orders[i] <= 10001 <= k <= orders.length
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