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Waymo interview notes, page 8 of 9

Large Enterprises

Most of these Waymo write-ups come from Software Engineers (46), then Machine Learning Engineers (23) and Data Scientists (10). The most reported rounds are the technical phone screen (58), the system design round (11), the behavioral / leadership round (6) and the hiring manager screen (4).

87 interview notes · updated Jul 2026

Waymo·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Waymo focused entirely on data storage at scale. No coding, just a long open-ended conversation about storage choices and schema design. Felt more like a senior architecture discussion than a typical SWE interview.
  • How would you decide between a relational database, a NoSQL store, and a columnar warehouse for storing large volumes of data?
  • Given a set of access patterns, how would you design the schema including entities, relationships, indexes, and partitioning keys?
  • How do you balance schema and storage design for a workload that needs to support both real-time writes and heavy analytics queries?

“This is the kind of question where you think you know the answer until you're actually saying it out loud.”

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Waymo·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
Waymo ML engineer system design round, one big open-ended question about inference serving at massive scale. Felt like a gauntlet that kept expanding the more I answered.
  • Design a production inference serving system for an ML model that needs to support 100 million daily active users. Walk through traffic and QPS estimates, memory requirements for weights and caches, network and accelerator bandwidth, hardware selection, latency optimization, and OOM failure handling.

“This question sprawls in every direction and I underestimated how much ground it actually covers.”

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Waymo·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Waymo ML engineer interview that went deep on model efficiency and training techniques. Four meaty topics back to back, each with a 'how would you evaluate this in production' follow-up that I was not fully ready for.
  • Explain quantization-aware training: what it is, why you'd use it, how it works, common pitfalls, and how you'd measure its impact on a production model.
  • Walk through knowledge distillation: the purpose, the mechanics, when it's the right tool, what can go wrong, and how you'd verify it actually helped in a real deployment.
  • What is evaluation mode in deep learning frameworks, why does it matter, when do people get it wrong, and how would you catch bugs related to it in a production pipeline?
  • Describe contrastive learning: the core idea, how it's trained, what scenarios it's useful for, what tends to go wrong, and how you'd evaluate whether it improved a production model.

“I knew the basics but fumbled on the pitfalls section.”

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Waymo·Software Engineer·Technical Phone Screen

Jun 2026
Phone screen for a software engineer role at Waymo. The technical portion was an object-oriented design problem built around a real-ish business scenario, and the interviewer seemed way more interested in how I was thinking through the problem than whether I got to a clean solution fast.
  • Design a class that processes two types of timestamp-based signals and returns continuous time intervals satisfying certain conditions.

“The problem sounds manageable until you realize there are a bunch of edge cases around how the two signal types interact near interval boundaries.”

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Waymo·Software Engineer·Technical Phone Screen

Jun 2026
Waymo SWE interview that centered on a classic frequency counting problem, but don't let that fool you. They pushed hard on knowing multiple approaches cold and being able to reason through the tradeoffs on the spot.
  • Given an array of elements, count how many times each element appears. Walk through multiple approaches and discuss the complexity and tradeoffs of each.

“Started with the hash map solution which is the obvious answer, O(n) time and O(k) space where k is distinct values.”

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Waymo·Data Scientist·Technical Phone Screen

Jun 2026
Waymo data scientist interview, SQL-heavy technical screen with four progressively harder questions all built around the same ride-hailing schema. The last two questions were genuinely tricky and I don't think I fully nailed either of them.
  • Given a users table and a rides table for a ride-hailing product, count the number of users who took zero rides during a specific 7-day window (July 1 through July 7, 2024, inclusive).
  • For each calendar month that had at least one ride, compute the total number of rides and the average ride rating rounded to two decimal places. Return results sorted by month descending.
  • Count the number of distinct users who have taken 0 or 1 total rides across their entire lifetime, grouped by city. Users who never took a ride must be included in the count.
  • For each user, rank the calendar months in which they had at least one ride chronologically (skipping months with no rides). Then compute the average ride rating across all rides that occurred in users' 1st and 3rd active months, grouped by city.

“Pretty straightforward once you remember to left join from users and filter on the ride side for nulls.”

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Waymo·Software Engineer·Onsite - Cross-functional / Panel

Jun 2026
Waymo's data fluency round is its own beast. There's barely any prep material out there for it, and the interviewer spent so much time on background context that I barely had room to actually answer anything. Left feeling pretty unsure about how it went.
  • What metrics would you use to measure progress in autonomous driving development?
  • Given two sets of experimental data from different approaches, which one performs better and why?

“This one tripped me up more than I expected.”

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Waymo·Software Engineer·Technical Phone Screen

Jun 2026
Waymo SWE interview with a string manipulation problem. Pretty standard coding round, nothing too wild, but the question had a specific approach they seemed to want.
  • Given two strings, one defining a custom character ordering and one as the target, rearrange the target string so its characters appear in the order defined by the first string. Characters not present in the ordering string can go anywhere.

“The core idea clicks pretty fast: count frequencies in the target, then walk through the ordering string and append each character the right number of times, then tack on whatever's left.”

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Waymo·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Waymo PM interview with a product strategy question about expanding a simulation tool to a new market. Not a lot of behavioral fluff, just one meaty scenario that required thinking through logistics, localization, and stakeholder alignment all at once.
  • How would you approach launching a simulation tool in a new geographic locale?

“I went straight to market sizing and realized pretty quickly that was the wrong entry point for a simulation tool.”

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Waymo·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Waymo ML Engineer interview that came down to a classic string problem, but they pushed through all three solution tiers which made it feel more like a depth check than a coding warmup.
  • Given a string, find the longest palindromic substring. Walk through your approaches from brute force to optimal, and explain the trade-offs at each step.

“Started with the O(N^3) brute force just to show I wasn't going to skip steps, then moved to expand-around-center which got me to O(N^2) with constant space.”

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