DoorDash Interview Questions — Page 2

DoorDash·Software Engineer·Online Assessment (OA)

Apr 2026

Bombed a first round with DoorDash and I'm still not fully over it. They said HackerRank and I assumed it'd be a standard coding problem, but they had me write code by hand with no IDE, no autocomplete, nothing. Eight years of switching languages and leaning on tooling caught up to me in the worst way.

Implement a solution to an open-ended coding problem by writing code by hand, without an IDE or autocomplete.

“I froze on syntax I genuinely use all the time but never type manually.”

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DoorDash·Software Engineer·Technical Phone Screen

Apr 2026

DoorDash coding screen focused on a dasher payment calculation problem. The constraint about no auxiliary data structures was the real twist, and the edge case conversation got uncomfortable fast.

Design and implement an API that ingests a stream of delivery driver events (shift start, pickup, dropoff, shift end with timestamps) and computes the driver's total payment for a shift using a rule like base pay per delivery plus per-mile plus per-minute, with minimum guarantees. You must process events in a single pass using only a small number of running counters, no arrays, sets, or hashmaps.

“The single-pass constraint is where I tripped up initially.”

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DoorDash·Machine Learning Engineer·Technical Phone Screen

Apr 2026

DoorDash ML engineer interview, open-ended case style where they drop a domain problem on you and expect a full walk-through from framing all the way to monitoring. No coding, just a long conversation about how you'd actually build the thing.

How would you frame an ML problem in the DoorDash domain (like ETA prediction or dasher dispatch), and what metrics would you use to measure success?
What data sources and features would you use to build a model for this problem?
Walk through your model selection process: what baselines would you start with, and when would you consider moving to deep learning?
How would you handle training and evaluation methodology, particularly around avoiding data leakage or temporal issues?
What deployment considerations would you think through, specifically around latency and keeping the model predictions fresh?
How would you design an A/B test to evaluate your model in production?
How would you monitor the model after deployment, and what failure modes would you watch for?

“This is where I spent too long hedging.”

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DoorDash·Software Engineer·Technical Phone Screen

Apr 2026

DoorDash coding interview focused entirely on a shopping cart discount system, broken into progressively harder parts. The OOD angle was the real challenge, not just the logic itself.

Design a shopping cart system with a promotion engine that selects the single best discount from a list of promotions. Promotions can be percentage-based (applied to item subtotal only) or fixed-amount (applied to the total including shipping). Return the final amount the customer pays after the optimal promotion is applied.
Extend the promotion system to handle edge cases: minimum spend thresholds before a promotion activates, promotions that apply only to specific items rather than the whole cart, and rules preventing promotions from stacking.

“The first part seemed manageable until I realized percentage discounts skip shipping and fixed-amount ones don't.”

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DoorDash·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026

Behavioral round at DoorDash for a software engineering role. Just one question but they really wanted the full story, not a quick answer.

Tell me about a conflict you had at work, whether with a manager, peer, or another team. Walk through what caused it, how you tried to understand the other side, how it got resolved, and what you took away from it.

“They weren't just looking for 'we talked it out and everything was fine.' They pushed on the trade-offs I actually weighed and why I made the calls I did.”

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DoorDash·Software Engineer·Technical Phone Screen

Apr 2026

DoorDash software engineer interview with a follow-up coding question that involved reasoning about how a data structure gets built. Short session, interviewer confirmed the approach without much back-and-forth.

Given a list where each element represents the profit of a dish and its index represents the dish's difficulty level, how would you generate this list?

“The setup clicked pretty fast once I mapped the index-as-difficulty constraint.”

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DoorDash·Software Engineer·Technical Phone Screen

Apr 2026

Analytics Engineer round at DoorDash that was basically one long deep-dive into a single fitness app scenario. They started with data modeling and just kept pulling the thread from there, metrics definitions, SQL, window functions, the whole chain.

Design the data model for a fitness app covering entities like users, workouts, workout sessions, devices, and subscriptions.
Define DAU for a fitness app. Which user event counts as 'active', how do you handle deduplication within the window, and how do you deal with time zones?
Given your proposed schema, write a SQL query that returns DAU per day for the last 30 days.
Extend your approach to compute rolling WAU and MAU, build a retention curve, and calculate stickiness (DAU/MAU). Use window functions where appropriate.

“Started okay, got the core tables down pretty fast.”

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DoorDash·Product Manager·Onsite - Product Sense / Strategy

Apr 2026

PM interview at DoorDash with a product sense question that felt a bit out of left field since it was about Spotify, not DoorDash. One question, no real context given beforehand.

How would you improve user engagement for Spotify?

“Threw me a bit because I was prepping for food delivery product questions and then they pull out Spotify.”

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DoorDash·Product Manager·Onsite - Product Sense / Strategy

Apr 2026

DoorDash PM interview with a pricing strategy question that sounds like a Walmart ops problem but is really testing whether you can think through second-order demand effects. Weird framing, but kind of interesting once you sit with it.

You manage pricing at physical Walmart stores. How would you grow revenue by lowering prices?

“My first instinct was to just say volume offsets the margin hit, which is fine but pretty surface level.”

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DoorDash·Machine Learning Engineer·Onsite - Behavioral / Leadership

Apr 2026

DoorDash ML Engineer behavioral round, and it was wall-to-wall negative prompts. Every single question was about something going wrong: conflicts, mistakes, failures. Not a single 'tell me about a win' in sight, which honestly threw off my pacing a bit.

Tell me about a time you had a conflict with a coworker or manager. How did it get resolved?
Describe a significant mistake you made at work. What did you do about it, and what changed afterward?
Walk me through a project that failed or fell short of its goal. What was your role, and what would you do differently?

“I had a story ready but it was maybe too tame.”

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