DoorDash Placement Papers 2026 with Solutions

Meta Description: Prepare for DoorDash 2026 campus drive with latest placement papers, exam pattern analysis, and 20+ solved questions. Get ready to crack the food delivery and logistics leader's hiring process.

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DoorDash is a technology company that connects consumers with their favorite local and national businesses in over 7,000 cities across the United States, Canada, Australia, Japan, and Germany. Founded in 2013 by Tony Xu, Stanley Tang, Andy Fang, and Evan Moore, DoorDash has become the leading food delivery platform in the United States, expanding beyond food to include grocery, retail, and convenience store delivery. With a mission to empower local economies, DoorDash builds products and services that help merchants grow, dashers earn, and consumers get what they need.

DoorDash's engineering culture emphasizes operational excellence, data-driven decision making, and solving complex logistics problems at scale. If you're targeting DoorDash placements in 2026, this comprehensive guide with real placement paper questions and detailed solutions is your ultimate preparation resource.

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DoorDash Hiring Pattern 2026

Eligibility Criteria

Parameter	Requirements
Degree	B.E./B.Tech/M.E./M.Tech/MCA/M.Sc (CS/IT)
Academic Requirement	Strong CS fundamentals; distributed systems knowledge is a plus
Backlogs	No active backlogs
Gap Criteria	Flexible, evaluated case-by-case
Package	₹20-40 LPA (India) / $130K-200K (US)

Selection Process Overview

1. Online Assessment (90 minutes)
2. Technical Phone Screen (45-60 minutes)
3. Virtual Onsite - Coding Round (60 minutes)
4. Virtual Onsite - System Design (60 minutes)
5. Virtual Onsite - Behavioral (45 minutes)

DoorDash Online Assessment Pattern 2026

Section	Number of Questions	Time	Difficulty
Aptitude & Problem Solving	15	25 minutes	Medium
Technical MCQs	10	15 minutes	Medium-High
Coding Problems	2-3	50 minutes	High
Total	27-28	~90 mins	-

Note: DoorDash emphasizes algorithms, data structures, and system design for real-time logistics.

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DoorDash Placement Papers 2026 - Practice Questions

Section 1: Quantitative Aptitude

Section 2: Logical Reasoning

Section 3: Technical MCQs

Section 4: Coding Problems

Question 16

Problem: Implement a delivery route optimization algorithm that finds the shortest path to deliver multiple orders.

Solution:

import heapq
from typing import List, Tuple, Dict

class DeliveryRouteOptimizer:
    def __init__(self):
        self.graph = {}  # location -> list of (neighbor, distance)
    
    def add_location(self, location: str):
        """Add a delivery location"""
        if location not in self.graph:
            self.graph[location] = []
    
    def add_road(self, location1: str, location2: str, distance: float):
        """Add a road between two locations"""
        if location1 not in self.graph:
            self.add_location(location1)
        if location2 not in self.graph:
            self.add_location(location2)
        
        self.graph[location1].append((location2, distance))
        self.graph[location2].append((location1, distance))
    
    def shortest_path(self, start: str, end: str) -> Tuple[List[str], float]:
        """Find shortest path using Dijkstra's algorithm"""
        if start not in self.graph or end not in self.graph:
            return [], float('inf')
        
        # Priority queue: (distance, current_location, path)
        pq = [(0, start, [start])]
        visited = set()
        
        while pq:
            current_dist, current_loc, path = heapq.heappop(pq)
            
            if current_loc in visited:
                continue
            
            visited.add(current_loc)
            
            # Check if we reached destination
            if current_loc == end:
                return path, current_dist
            
            # Explore neighbors
            for neighbor, edge_dist in self.graph[current_loc]:
                if neighbor not in visited:
                    new_dist = current_dist + edge_dist
                    new_path = path + [neighbor]
                    heapq.heappush(pq, (new_dist, neighbor, new_path))
        
        return [], float('inf')
    
    def optimize_delivery_route(self, start: str, deliveries: List[str]) -> List[str]:
        """
        Optimize delivery route for multiple deliveries (Traveling Salesman Problem approximation)
        Uses nearest neighbor heuristic
        """
        if not deliveries:
            return [start]
        
        current = start
        unvisited = set(deliveries)
        route = [start]
        total_distance = 0
        
        while unvisited:
            # Find nearest unvisited delivery
            nearest = None
            min_dist = float('inf')
            
            for delivery in unvisited:
                path, dist = self.shortest_path(current, delivery)
                if dist < min_dist:
                    min_dist = dist
                    nearest = delivery
            
            if nearest is None:
                break
            
            # Add to route
            route.append(nearest)
            total_distance += min_dist
            current = nearest
            unvisited.remove(nearest)
        
        # Return to start
        path_back, dist_back = self.shortest_path(current, start)
        total_distance += dist_back
        
        return route, total_distance
    
    def multiple_dasher_routing(self, dashers: List[str], deliveries: List[str]) -> Dict[str, List[str]]:
        """
        Assign deliveries to multiple dashers optimally
        Simple greedy assignment
        """
        # Sort deliveries by distance from each dasher's starting point
        assignments = {dasher: [] for dasher in dashers}
        
        for delivery in deliveries:
            # Find dasher closest to this delivery
            best_dasher = None
            best_distance = float('inf')
            
            for dasher in dashers:
                path, distance = self.shortest_path(dasher, delivery)
                if distance < best_distance:
                    best_distance = distance
                    best_dasher = dasher
            
            if best_dasher:
                assignments[best_dasher].append(delivery)
        
        return assignments

# Test
optimizer = DeliveryRouteOptimizer()

# Add locations and roads
locations = ["Restaurant", "Customer1", "Customer2", "Customer3", "Customer4"]
for loc in locations:
    optimizer.add_location(loc)

# Add roads with distances
optimizer.add_road("Restaurant", "Customer1", 2.5)
optimizer.add_road("Restaurant", "Customer2", 3.0)
optimizer.add_road("Customer1", "Customer2", 1.5)
optimizer.add_road("Customer2", "Customer3", 2.0)
optimizer.add_road("Customer3", "Customer4", 1.0)
optimizer.add_road("Customer1", "Customer4", 2.5)

# Test shortest path
path, distance = optimizer.shortest_path("Restaurant", "Customer4")
print(f"Shortest path: {path}, Distance: {distance}")

# Test delivery route optimization
deliveries = ["Customer1", "Customer2", "Customer3", "Customer4"]
route, total_dist = optimizer.optimize_delivery_route("Restaurant", deliveries)
print(f"Optimized route: {route}, Total distance: {total_dist}")

# Test multiple dasher routing
dashers = ["Dasher1", "Dasher2"]
assignments = optimizer.multiple_dasher_routing(dashers, deliveries)
print(f"Dasher assignments: {assignments}")

Time Complexity: O(V²) for nearest neighbor TSP approximation
Space Complexity: O(V + E) for graph storage

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Question 17

Problem: Design a real-time order tracking system for DoorDash.

Solution:

from datetime import datetime
from enum import Enum
from typing import Dict, List, Optional
import asyncio

class OrderStatus(Enum):
    PLACED = "placed"
    CONFIRMED = "confirmed"
    PREPARING = "preparing"
    READY = "ready"
    PICKED_UP = "picked_up"
    ON_THE_WAY = "on_the_way"
    DELIVERED = "delivered"
    CANCELLED = "cancelled"

class OrderEvent:
    def __init__(self, order_id: str, status: OrderStatus, timestamp=None, location=None, dasher_id=None):
        self.order_id = order_id
        self.status = status
        self.timestamp = timestamp or datetime.now()
        self.location = location  # Optional: GPS coordinates
        self.dasher_id = dasher_id  # Optional: Dasher assigned
    
    def to_dict(self):
        return {
            "order_id": self.order_id,
            "status": self.status.value,
            "timestamp": self.timestamp.isoformat(),
            "location": self.location,
            "dasher_id": self.dasher_id
        }

class OrderTracker:
    def __init__(self):
        self.orders = {}  # order_id -> list of OrderEvent
        self.subscribers = {}  # order_id -> list of callback functions
        self.status_handlers = {}  # status -> handler function
    
    def place_order(self, order_id: str, customer_id: str, restaurant_id: str, items: List[str]):
        """Place a new order"""
        if order_id in self.orders:
            raise ValueError(f"Order {order_id} already exists")
        
        initial_event = OrderEvent(order_id, OrderStatus.PLACED)
        self.orders[order_id] = {
            "customer_id": customer_id,
            "restaurant_id": restaurant_id,
            "items": items,
            "events": [initial_event],
            "current_status": OrderStatus.PLACED,
            "created_at": datetime.now()
        }
        
        self._notify_subscribers(order_id, initial_event)
        return order_id
    
    def update_status(self, order_id: str, status: OrderStatus, **kwargs):
        """Update order status"""
        if order_id not in self.orders:
            raise ValueError(f"Order {order_id} not found")
        
        order = self.orders[order_id]
        
        # Validate status transition
        if not self._is_valid_transition(order["current_status"], status):
            raise ValueError(f"Invalid status transition: {order['current_status']} -> {status}")
        
        # Create event
        event = OrderEvent(order_id, status, **kwargs)
        order["events"].append(event)
        order["current_status"] = status
        
        # Call status handler if exists
        if status in self.status_handlers:
            self.status_handlers[status](order_id, order)
        
        # Notify subscribers