DoorDash Insight

Offer

New Grad Software Engineer

1-3 Years

Jul 29, 2026

Process Overview

Applied through careers page, followed by a recruiter call, online assessment, technical phone screen, and a virtual onsite loop.

Evaluation Stages

Recruiter Call

⏱ 30 mins

LogisticsBackground

The call focused on candidate background, motivation for joining DoorDash, and logistical preferences regarding location and team placement.

Questions Asked

Why DoorDash?

Behavioral

Online Assessment

⏱ 90 mins

Data StructuresAlgorithms

The assessment required clean, runnable code with consideration for edge cases.

The first problem involved a sliding window technique to find peak delivery volume.

The second problem required modeling a driver assignment system using a greedy approach with a min-heap.

Questions Asked

Find the peak delivery window of size K given a list of orders with timestamps.

Sliding WindowArrays

Reference

Assign drivers to orders based on proximity and availability.

GreedyHeaps

Reference

Technical Phone Screen

⏱ 45 mins

Data StructuresSystem Design

The candidate implemented a merchant rating system using a hash map of deques to manage time-based expiration of reviews.

The interviewer probed for time complexity optimizations.

The discussion evolved into a bucketing approach by day to handle data more efficiently.

Questions Asked

Design a merchant rating system that supports adding reviews and calculating the average rating over the last N days.

Hash MapDequeSystem Design

Virtual Onsite - Coding

⏱ 60 mins

GraphsBitmasking

The problem required finding the shortest path for a driver to visit multiple locations and return to a depot.

The solution involved BFS combined with bitmasking to track the state of picked-up orders.

The candidate successfully implemented the logic but faced time constraints regarding full optimization.

Questions Asked

Find the shortest path for a driver to pick up multiple orders in a grid and return to the depot.

GraphsBFSBitmasking

Reference

Virtual Onsite - System Design

⏱ 45 mins

System DesignScalability

The candidate proposed a architecture involving WebSockets for real-time updates and Kafka for location ingestion.

Geohashing was discussed as a strategy for managing driver locations at scale.

The discussion included trade-offs between consistency and latency, demonstrating an understanding of distributed systems.

Questions Asked

Design the backend for DoorDash's real-time order tracking system.

System DesignScalability

Virtual Onsite - Behavioral

⏱ 45 mins

Behavioral

The interview followed the STAR (Situation, Task, Action, Result) format.

The interviewer focused on conflict resolution and the ability to learn new technologies quickly.

Questions Asked

Tell me about a time you disagreed with a teammate.

Behavioral

Describe a project where you had to learn something new quickly.

Behavioral

Executive Insights

Candidate Advice

Do not overthink the interview process; interviewers are generally supportive and provide hints when candidates get stuck.
Prioritize preparation for behavioral rounds as they are a significant part of the evaluation.
Focus on demonstrating the ability to reason about scale and trade-offs during system design interviews.

Preparation Tips

Practice sliding window, graph algorithms, and priority queue problems extensively.
Understand fundamental system design concepts such as load balancers, message queues, caching, and database sharding, even for new grad roles.
Prepare behavioral stories using the STAR method.