Waymo Interview Questions — Page 3

Waymo·Software Engineer·Technical Phone Screen

Apr 2026

Waymo SWE interview with a binary search problem that looked straightforward until the edge cases started stacking up. The encoded string constraint was the interesting part.

You're given a run-length encoded string as (character, count) pairs where the characters are guaranteed to be in non-decreasing sorted order. Implement a function FindByValue(target, left, right) that, within the decoded string's index range [left, right], returns the index of the first character strictly greater than the target. Return -1 if none exists in that range.

“The sorted-characters guarantee is what makes this tractable.”

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Waymo·Software Engineer·Onsite - System Design / Architecture

Apr 2026

Waymo system design round, one big question that sprawled into six different sub-topics. Felt like they were stress-testing how far you could go on a single problem rather than checking boxes. Left the room unsure if I'd covered enough depth on any one area or just skimmed all of them.

Design an end-to-end evaluation system for a self-driving model, covering offline replay pipelines, simulation, sim-to-real gap detection, parallel compute at scale, simulation acceleration, and production bug handling.
How would you design a simulation platform that can generate synthetic road scenarios including rare or dangerous events like jaywalkers, sudden braking, weather shifts, and sensor dropout?
What techniques would you use to detect when simulated objects or environments don't look realistic enough, and how would you measure the sim-to-real gap?
How do you run thousands of simulations in parallel when compute is constrained? Walk through your approach to prioritizing which scenarios to run.
What strategies would you use to speed up simulation without sacrificing the quality of results for safety-critical scenarios?
How would you handle bugs that surface in the deployed product itself? Describe your approach to fault injection, regression testing, and release gating.

“This was basically six questions dressed up as one.”

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Waymo·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Debugging round at Waymo for an ML Engineer role, paired with an American interviewer. The session covered numpy, tensor ops, and distributed computing bugs. Pretty sure I bombed it.

Given a matrix initialized with Matrix.zeros, identify the bug caused by aliasing behavior.
Debug a to_ndarray conversion and determine whether a missing axis=1 argument is causing incorrect output.
Trace through a from_ndarray usage and find where the conversion logic breaks down.
Find the bug related to truncating remainders in a distributed tensor computation.

“Stared at this longer than I should have.”

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Waymo·Software Engineer·Technical Phone Screen

May 2026

Waymo coding round for a software engineer role. One problem, quiet interviewer, and a moment where I genuinely wasn't sure I'd get through it.

Given a set of points on a 2D plane, find the maximum area rectangle where all four corners are points in the set and the sides are axis-aligned.

“It's a spin on a classic problem but flipped to maximize instead of minimize.”

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Waymo·Software Engineer·Technical Phone Screen

May 2026

Did an additional round at Waymo for a software engineering role. The interviewer was friendly enough but spent a weirdly long time explaining the autonomous driving stack to someone who already had relevant experience, and by the time we got to the actual question I could already feel it wasn't going well.

You have two sets of experiments with different latency profiles in the planning module. How do you decide which configuration is better?

“This is a deceptively tricky question because it's not just 'pick the lower latency one.' You have to think about what latency even means in a planning context, tail latencies versus medians, how latency interacts with safety margins, whether the two experiment setups are even comparable.”

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Waymo·Data Scientist·Technical Phone Screen

May 2026

Waymo data scientist interview with a pretty heavy computational geometry problem. Not what I expected going in, felt more like a physics/algorithms hybrid than anything data-sciency.

Given n cars on a 1D road, each with an initial position, velocity, constant acceleration, and a physical radius, determine whether any two cars collide for t >= 0. If a collision exists, return the earliest collision time and the pair involved.
Extend the collision detection to 2D: each car moves on a flat plane with a 2D position, velocity, and acceleration vector, plus a radius. Find the earliest collision time across all pairs.
The naive approach checks all pairs in O(n^2). How would you optimize collision detection for large n, say up to 10^5 cars?
How do you handle the edge case where two cars have identical trajectories and overlap for an entire time interval rather than a single point?

“The math itself isn't too bad once you set it up.”

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Waymo·Machine Learning Engineer·Onsite - Behavioral / Leadership

May 2026

Director-level behavioral round for an MLE role at Waymo. Three questions, all fairly meaty, focused on how you think about strategy vs. execution, your biggest past project, and how you handle policy-versus-speed trade-offs. Not a casual chat.

Tell me about a project where you had to balance high-level strategic goals with the day-to-day execution details. How did you keep yourself grounded in the details without losing sight of the bigger picture?
Walk me through your most impactful project. Cover the problem, your specific role, the key decisions you made, and what the outcome was.
Describe a situation where following a policy (safety, fairness, compliance, etc.) was in direct tension with efficiency or hitting a business metric. How did you frame that trade-off and what happened?

“This one tripped me up a little because I went too macro too fast.”

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Waymo·Software Engineer·Technical Phone Screen

May 2026

Waymo SWE interview with a grid partitioning problem that exposed a real gap in my ability to handle randomness constraints under pressure. I had the right instinct but the wrong execution, and only figured out the proper approach after the fact.

Given an m×n grid and 4 tokens (numbered 1 to 4), write an algorithm to randomly populate the grid so that each token occupies exactly the same number of cells, each token's region is contiguous (4-directional connectivity), and the entire grid is covered.

“I went straight to BFS and got partway there but completely fell apart on the randomness part.”

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Waymo·Data Scientist·Technical Phone Screen

May 2026

Waymo Data Scientist screen, pretty straightforward Python fundamentals. One coding question, nothing that required deep ML knowledge, which surprised me a bit.

Write a Python function that computes the arithmetic mean of a list of numbers, returning 0 for an empty list and without modifying the input.

“Seemed almost too easy so I second-guessed myself for a second.”

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

May 2026

Waymo ML engineer system design round, single question but it had a lot of moving parts. The focus was on designing an inference system at massive scale and the math-heavy estimation portion took up more time than I expected.

Design an ML inference system for 100 million daily active users. Include back-of-envelope estimates for memory and bandwidth, and address latency optimization and out-of-memory risks.

“The estimation part tripped me up more than the architecture itself.”

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