DoorDash's engineering loop is structured around a small, well-known question bank and three onsite specialties: CodeCraft (production-style coding, sometimes AI-assisted), Debugging (fix a broken Dasher-adjacent service), and System Design paired with a Domain / Deep-Dive round. A tech screen (often a DoorDash-flavoured LeetCode-tagged question) gates the onsite; the onsite is typically CodeCraft + Debugging + System Design/Domain + Hiring-Manager or Values. As of 2026, DoorDash publicly announced it is replacing traditional algorithmic coding rounds with 60-minute AI-assisted engineering working sessions where candidates use Cursor/Copilot/Claude Code on realistic DoorDash-shaped tasks. Note: DoorDash's Dasher (delivery driver) sign-up is NOT a conventional interview - it is an app-based application, ID check, and background check with no interview round.
These 7 writeups cover software engineering, data engineering, ios engineering roles. All of them were posted under a pseudonym (Reddit, GeeksforGeeks or forum handles), so we could not verify the authors’ identities.
Typical rounds
2.71-5 range
Outcomes shared
1/2offer / not
Most common round
Technical
Sources span
2025 - 2026
Most frequently reported · System design(5), API design(4), Data structures & algorithms(4), Debugging(4), Behavioral(3)
DoorDash’s official process
DoorDash publicly announced (March 2026) that it is replacing algorithmic coding rounds with a 60-minute AI-assisted engineering working session. Candidates code on their own machine in their preferred IDE with editor-integrated AI (Cursor, Claude Code, Codex, Copilot); all AI features (chat, inline, agent mode, running commands) are allowed and expected. Starter code and a DoorDash-shaped task (e.g. extend an order-dispatch system, build a smart menu composer, automate a support-ticket workflow) are provided. Candidates share their screen and narrate. Scoring signals: orientation in an unfamiliar codebase, effective AI use and output verification, debugging, scope management, and communication of trade-offs; finishing every task is less important than tight loop and judgment. DoorDash also sends a candidate guide up front setting expectations, and has moved to smaller focused projects after pilots showed a full microservice question was too ambitious for 60 minutes.
System design·Debugging·Trees·Graphs·Dynamic programming·Low-level design·API design·Behavioral
Interview experiences at DoorDash
Outcome
Senior Software Engineer (E5), Backend
Experienced
Outcome not shared4 rounds
1
Phone screen (coding)Phone screen
A variation of LeetCode's Binary Tree Maximum Path Sum, with the interviewer rewording nodes as domain concepts and asking for follow-ups on edge cases and complexity.
Covered · Trees, Data structures & algorithms
2
System Design + DomainSystem design
Deep dive into the candidate's most challenging project and a specific hard problem, then a design of a Yelp-style reviews system with the usual functional/non-functional negotiation.
Covered · System design, Past projects, API design
3
CodeCraft (coding)Technical
Given a stream of Dasher pickup and dropoff events (timestamp, status), compute how much a Dasher should be paid, with a concurrency multiplier that increments as the Dasher takes on more concurrent orders and decrements as they drop them off. Extensive edge-case follow-ups.
Covered · Low-level design, OOP, Domain knowledge
4
DebuggingTechnical
Find and fix a bug in a Dasher-assignment class that is effectively a buggy insert/delete/getRandom in O(1) implementation - a random index is popped from a map, then the last entry is meant to be swapped in, but the length check happens after the pop and off-by-one corrupts the map.
A graph/DSU problem (Number of Provinces). The interviewer accepted a DFS solution and asked about time/space complexity of DFS vs DSU.
Covered · Graphs, Data structures & algorithms
2
Coding (DSA)Technical
Next Greater Element III, with the expectation to first walk a brute-force recursive solution and then the optimal one, both implemented in the round.
Covered · Arrays & strings, Data structures & algorithms
3
System Design and Domain KnowledgeSystem design
Discussion of current project followed by designing a Review System. Clarified functional and non-functional requirements; the interviewer offered little guidance during the design walkthrough.
Covered · System design, Past projects
4
Coding (DSA)Technical
Search Suggestions System - proposed sorting plus binary search and a trie, compared complexities, implemented the trie version and walked edge cases.
Covered · Trees, Data structures & algorithms
5
ValuesBehavioral
Introductions, discussion of current project, and standard behavioural questions.
Zoom call with the recruiter walking through the JD and India teams, background chat about experience and interest.
Covered · Behavioral
2
Tech screenPhone screen
Coding problem involving 2D coordinates, solved with a hash map and binary search to find the nearest coordinate.
Covered · Arrays & strings, Data structures & algorithms
3
Bug Bash (Debugging)Technical
Small codebase provided in C++ (Java/Python/Go were the offered languages; JavaScript was not); read the code, run it, spot and fix bugs, then a high-level discussion of scaling and multithreading options.
Covered · Debugging, Concurrency
4
System Design and DomainSystem design
After a most-challenging-problem deep dive, designed a Job Scheduler. The interviewer pushed for the main design early, then repeatedly probed on missing tables, indexes and partition keys.
60-minute AI-assisted coding session using a local IDE with Copilot/Cursor/Claude Code. Task: extend a workflow-engine skeleton that automates DoorDash self-help tickets (e.g. late order, missing item). Given dataclasses for OrderModel, a MockDoorDashAPI, a Node/NodeType/Workflow DAG and a stubbed WorkflowEngine; implement the stubs to pass provided tests, then add partial-refund logic when the order is only slightly late, then investigate and improve a slow 10-20s execution path. The panel explicitly evaluates prompt-engineering ability and rewards using AI as an assistant rather than as a crutch.
Covered · Low-level design, API design, Debugging, OOP
One-hour design of a charity-donation app: 10 charities, a 3-day event, ~10M donations and ~$100M expected. Frontends already exist; integrate the Braintree Payments REST API, handle a spike load, notify users on successful donation, and reason through failure modes and retry/idempotency. Disbursement is out of scope (CFO cuts a manual check afterwards).
A single-file mock Dasher-assignment service with classes for User, Dasher, a stubbed RemoteDeliveryRecordingService, and a DeliveryAssignmentService with addDasher, pickKey (random index), adjustMap and pickDasher already implemented. Read the business spec, find the small but important logic bugs, and improve the code. Follow-ups on additional test cases and a cleaner implementation. No AI allowed for this round.
Author reports DoorDash uses a very small CodeCraft question bank (roughly two or three questions), so targeted practice from recent interview writeups is more efficient than volume LeetCode.
Covered · Data structures & algorithms, Low-level design
2
System DesignSystem design
Practised against the company's recently asked SD questions and rehearsed the interview flow with an AI stand-in interviewer.
Covered · System design, Domain knowledge
3
BehavioralBehavioral
Read DoorDash's core values and prepared STAR-style stories mapped to each.
DoorDash's engineering loop is structured around a small, well-known question bank and three onsite specialties: CodeCraft (production-style coding, sometimes AI-assisted), Debugging (fix a broken Dasher-adjacent service), and System Design paired with a Domain / Deep-Dive round. A tech screen (often a DoorDash-flavoured LeetCode-tagged question) gates the onsite; the onsite is typically CodeCraft + Debugging + System Design/Domain + Hiring-Manager or Values. As of 2026, DoorDash publicly announced it is replacing traditional algorithmic coding rounds with 60-minute AI-assisted engineering working sessions where candidates use Cursor/Copilot/Claude Code on realistic DoorDash-shaped tasks. Note: DoorDash's Dasher (delivery driver) sign-up is NOT a conventional interview - it is an app-based application, ID check, and background check with no interview round.
What topics does DoorDash test in interviews?
Commonly reported topics include System design, Debugging, Trees, Graphs, Dynamic programming, Low-level design.
These guides summarize public, first-hand interview experiences shared by candidates. They describe the shape of each loop and the topics that came up, not a leaked question bank, and every experience links back to its original source. Processes change often. Treat this as directional prep, not a script.