DoorDash Careers

US-based local-commerce and food-delivery platform, the largest of its kind in the United States, listed on Nasdaq as DASH.

doordash.com

About the company

Founded

2013

Headquarters

San Francisco, California, United States

Industry

Online food ordering and delivery (local commerce)

Employees

31,400 (2025)

Revenue (FY25)

$13.72 billion

Market Cap

~$77.65 billion (point in time)

Listed on

NASDAQ: DASH

CEO

Tony Xu, Co-Founder & Chief Executive Officer

CFO

Ravi Inukonda, Chief Financial Officer

Legal name

DoorDash, Inc.

DoorDash, Inc. is a US technology company that operates a local-commerce marketplace connecting consumers with restaurants, grocers, and other merchants for on-demand delivery and pickup. Founded in 2013 and headquartered in San Francisco, California, the company runs the largest food-delivery platform in the United States, holding roughly a 56% market share. It trades on the Nasdaq under the ticker DASH and is a component of the Nasdaq-100 and S&P 500 indexes.

The company was started by Tony Xu, Andy Fang, Stanley Tang, and Evan Moore, and is led today by co-founder Tony Xu as Chief Executive Officer and Chairman. Ravi Inukonda serves as Chief Financial Officer and Prabir Adarkar as President and Chief Operating Officer. DoorDash employed about 31,400 people as of 2025 and made its debut on the Fortune 500 list in 2024.

For fiscal year 2025, DoorDash reported revenue of about $13.72 billion, up roughly 28% from $10.72 billion in 2024, with net income of $935 million. The company operates a headquarters in San Francisco plus additional US offices in cities including New York, Phoenix, Los Angeles, Denver, Chicago, and Seattle, and international offices in markets such as Toronto, Tokyo, Mexico City, and Sao Paulo.

Key People

Tony Xu

Co-Founder, Chief Executive Officer & Chairman

Ravi Inukonda

Chief Financial Officer

Prabir Adarkar

President & Chief Operating Officer

Andy Fang

Co-Founder, Head of LaunchPad

Interview process at DoorDash

(20,361 interview reviews)

Interview experience

6/10 (favorable)

Interview difficulty

Easy (2/10)

Interview process length

Most within a day or two

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How long the process takes

About a day or two65%

About a week20%

About a month10%

More than one month5%

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DoorDash Interview Questions & Answers

Q: Find the k nearest restaurants to a user's location.

A: Treat it as a top-k problem: compute the distance from the user to each candidate and keep the k closest. A bounded max-heap of size k or a quickselect partition both work.

Answer by AI

Clarify the distance metric (straight-line vs road distance) and whether coordinates are pre-indexed.
Maintain a max-heap of size k so each candidate costs log k; pop when a closer one arrives. Overall O(n log k).
If restaurants are static and queried often, pre-build a spatial index (k-d tree or geohash grid) to prune candidates first.

Q: Schedule tasks for a single-threaded CPU and return the processing order.

A: Sort tasks by availability, then use a min-heap keyed on processing time to always pick the shortest ready task. Advance the clock when nothing is ready.

Answer by AI

Sort tasks by enqueue time so you know when each becomes available.
Push all currently available tasks into a min-heap ordered by processing time (tie-break by index), then pop the smallest.
Jump the clock forward to the next task's start time whenever the heap empties. Runs in O(n log n).

Q: Find the longest increasing path in a matrix.

A: Run DFS from every cell, moving only to strictly larger neighbors, and memoize the longest path length starting at each cell so each is computed once.

Answer by AI

For each cell, the answer is 1 plus the max over neighbors with a larger value.
Cache the result per cell so the whole grid is O(rows times cols); the strictly-increasing rule means no cycles.
Return the maximum cached value across all cells as the final answer.

Q: Build the Dasher payout logic for a new payments service that calls an upstream dependency.

A: Design the call to be idempotent and resilient: attach a unique request key, set timeouts, retry with backoff, and record state so a retry never double-pays.

Answer by AI

Make payouts idempotent with a unique key per payout so retries are safe.
Wrap the upstream call in timeouts, bounded retries with backoff, and a circuit breaker for sustained failures.
Persist the payout state (pending, sent, confirmed) so the system can reconcile after a crash or partial failure.