Candidates describe DoorDash Software Engineer loops that often include a recruiter call, a coding screen, and a virtual onsite with practical coding, debugging, design and behavioral conversations. Here is what they report and how to prepare.
$192K
Avg. Base Comp
$298K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describe the DoorDash Software Engineer interview as more practical than a standard algorithm loop. Most reports start with a recruiter or HR call about background and experience, followed by a coding phone screen.
The coding prompts lean toward delivery-domain problems. One candidate was asked to build a Dasher payout service that began with a single delivery and then grew to cover overlapping delivery windows and arbitrary time ranges. They ran out of time and wished they had spent less of it on the first version and more on planning for edge cases. Another described prompts such as dispatching Dashers or tracking active restaurant orders, with an expectation of fast, production-ready code.
One candidate's virtual onsite had four rounds: debugging, an AI-focused interview, system design, and a behavioral and project discussion. The debugging round tested whether they could reason through and improve existing code. Not every loop is the same, and another candidate reported LeetCode-style problems at medium to hard difficulty alongside behavioral and system design rounds.
When requirements grow mid-problem, narrate your edge cases and reasoning instead of stopping at a working solution. A few reports also describe a role being filled or paused around the first call, so confirm the opening is active and ask how many rounds remain before you invest in preparation.
Synthesized from 33 candidate reports by our editorial team.
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Real interview reports from people who went through the Doordash process.
The interview was friendly and conversational, but the coding problem became more complicated than I initially expected.
I was asked to implement a Dasher payout service that calculates payments for completed deliveries. The initial problem seemed manageable, but the interviewer gradually introduced additional requirements, including handling time-series data and calculating payouts for multiple orders within the same time window.
The tricky part was managing overlapping delivery windows and ensuring the payout calculations were accurate across multiple orders. I understood the requirements and had a general approach, but as the complexity increased, I struggled to complete the implementation within the allotted time.
The interviewer was supportive and gave me room to think through the problem. Overall, it was a good experience, but time management was my biggest challenge. I wish I had spent less time on the initial implementation and more time planning for the additional edge cases.
Questions asked: The coding question was to implement a Dasher Payout Service, similar to how DoorDash calculates earnings for delivery drivers.
Given a stream of delivery orders, each containing a Dasher ID, order ID, start time, end time, and payout rate, implement a service that calculates the total earnings for a Dasher within a specified time window.
The interviewer gradually added more requirements:
The most challenging part was designing an efficient way to track active deliveries over time and calculate payouts correctly when multiple orders overlapped. The problem started relatively simple but evolved into a more complex interval-processing and time-series aggregation problem.
I wasn't able to complete the full implementation within the interview time, but it was an interesting problem that tested data structures, algorithm design, and handling changing requirements.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Doordash
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Monthly Customer Report | |
| Over-Budget Projects | |
| Daily Retention Summary | |
| Delivery Estimate Model | |
| Month Over Month | |
| Average Order Value | |
| Google Maps Improvement | |
| Longest Streak Users | |
| Valid Anagram | |
| Hurdles In Data Projects | |
| Success Measurement | |
| How Many Friends | |
| Christmas Dinner Ingredient Optimization | |
| Count Transactions | |
| Celebrity Mentions | |
| Biggest Tip | |
| Data Pipelines and Aggregation | |
| Sample Time Series | |
| A/B Testing a Checkout Button Change | |
| A Simpler KNN From Scratch | |
| Uber Eats Customer Experience | |
| Safe Deployments | |
| Smart Fitness Tracker Launch | |
| Client Solution Pushback | |
| Ranking Metrics | |
| Cross-Culture Reports | |
| Decreasing Payments | |
| Statistically Significant Test | |
| Delivery Online |
Synthesized from candidate reports. Individual experiences may vary.
Candidates describe starting with a recruiter or HR reach-out and a call about background and experience. Two reports describe the role being filled: in one, the recruiter screen was canceled before it took place, and in the other, it led to an initial denial before a later final round. It is worth asking early whether the opening is still active.
The coding screen is usually the first technical step. One candidate reported LeetCode-style coding at medium to hard difficulty. Another passed the technical phone screen before the process was paused by hiring changes. One report mentions only a coding round, with no clear information on how many rounds followed.
Reported prompts include a Dasher payout service with overlapping delivery windows and time-series earnings, and problems around dispatching Dashers or tracking active restaurant orders. Requirements can grow as you go, so plan your structure early, leave time for edge cases, and keep your solution close to production quality.
One candidate described four onsite rounds: debugging, an AI-focused interview, system design, and a behavioral and project discussion. Another reported an onsite mixing behavioral, coding and system design interviews. Practice reading unfamiliar code quickly and explaining your reasoning as you fix issues.
One candidate had a hiring-manager round focused on behavioral questions before the onsite. Another described behavioral questions about their background early in the process and again in a later round. Prepare a few projects you can explain in depth, including tradeoffs and what you would change.
