Waymo Interview Questions (2026)
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1/4Waymo Internship Interview Experience and Questions
Python DataClass: Implement Basic Key-Value Store with Get/Set
Serialize and Deserialize Binary Tree with Character Values
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K-means Clustering Implementation
Count Elements in an Array
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Minimum Index Distance Between 1 and 2
Parse CSV String Into a Usable Data Structure (with Corruption Handling)
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#871 Minimum Number of Refueling Stops
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Closest Bus Stop: Find the Nearest Bus Stop to a Query Point Using Spatial Data
Waymo SWE Onsite - Knight Dialer
Waymo SWE Phone - Largest Rectangle
Waymo SWE Phone - Largest Rectangle from Points
Waymo SWE Phone - Maximal Square
Maximum Matching: Find Maximum Bipartite Matching Using Augmenting Paths
Waymo SWE Phone - Maximum Rectangle Area
Maximum Subgraph: Find the Largest Connected Subgraph Satisfying a Node Constraint
Waymo SWE Phone - Merge Schedule
Waymo SWE Onsite - Minimum Cost Connecting
Nearest Pair: Find the Closest Pair of Points in 2D Space
Waymo SWE Phone - Next Greater Node
Waymo Internship Interview Experience and Questions
Question Details
Round 1
Context The session began with a discussion regarding project background, followed by a coding challenge.
Problem Statement Given a list of tasks, where each task has a specific ID, deadline, and reward, determine the execution order that maximizes the total profit within the given time limits.
Solution Approach The problem is best solved using a greedy strategy: 1.
Sort: Arrange all tasks in descending order based on their reward value. 2.
Schedule: For each task, check for available time slots starting from the task's deadline and moving backward. 3.
Assign: If an empty slot is found, schedule the task in that slot. If no slot is available before the deadline, skip the task.
Round 2
Behavioral Assessment The first half of the interview focused on collaboration and influence within a team setting. Key scenarios discussed included: * Handling disagreements among team members. * Influencing project progress without possessing direct authority. * Strategies for unblocking a project that has stalled.
Problem Statement Write a function that accepts a data stream and an integer parameter, $n$. The output relies on the sign of $n$: * If $n > 0$:
Output the first $n$ elements of the stream. * If $n < 0$:
Output the last $|n|$ (absolute value of $n$) elements of the stream.
Solution and Optimization To handle the requirement for $n < 0$ efficiently, especially when dealing with very large data streams, memory usage must be minimized. The optimal solution involves using a circular buffer of fixed length $|n|$. This allows the system to store only the most recent chunk of data necessary for the output, discarding older elements as new ones arrive.
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More from Waymo
Waymo Interview Process Overview
The Waymo interview process typically includes a recruiter screen, one to two technical phone screens, and a 4-6 round on-site or virtual on-site loop. Each round serves a distinct calibration purpose: coding rounds measure correctness, code quality, and complexity reasoning; system design rounds measure architectural judgment at the appropriate level; behavioral rounds measure ownership, leadership scope, and collaboration. Reports tagged on LeakCode from 2024-2026 show Waymo runs a calibrated process consistent with industry norms for companies of its tier.
Difficulty calibration: Waymo coding rounds typically run medium difficulty with follow-up depth as the senior discriminator. System design rounds expect production-grade trade-off articulation at L4+ levels. Behavioral rounds expect quantified outcomes ("reduced p99 latency from 800ms to 120ms") rather than vague impact claims. The candidates who advance consistently demonstrate clear thinking out loud rather than perfect final answers.
How To Use Waymo Question Reports
Real candidate-reported interview questions are a calibration tool, not a memorization target. Waymo updates its question pool every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage approach: identify the patterns that appear repeatedly in Waymo reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions above by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Waymo's pool. Reports tagged with quantified difficulty and explicit round type are higher-signal than reports without those tags. The metadata filters help you build a focused study plan in 1-2 hours rather than 8-10 hours of unstructured browsing.
Common Waymo Interview Mistakes
Reports tagged "no hire" at Waymo consistently surface a few patterns: jumping into code without clarifying requirements, coding silently for extended periods, missing edge cases (empty input, single element, large input, overflow), producing working code the candidate cannot refactor when probed, and behavioral stories that use "we" instead of "I" diluting individual signal. Strong candidates explicitly avoid these patterns by following a consistent round template.
The single most predictive failure mode in recent reports: not asking clarifying questions. Interviewers are explicitly trained to weight this dimension. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into implementation immediately. Strong candidates also verbalize their approach before writing code; weak candidates code in silence and lose the communication dimension of the round's calibration.