Waymo Interview Questions — Page 1

Waymo·Data Scientist·Technical Phone Screen

Jul 2024

Waymo data scientist interview that was pretty much a single extended case study on A/B testing for a routing algorithm. The whole thing revolved around experiment design and statistical validity, which I thought I knew well until the follow-ups made me second-guess myself.

Write Python code to filter ride data to San Francisco in July 2024, join it to experiment assignments, run a Welch two-sample t-test comparing treatment vs. control time-to-pickup, and return the p-value.
Given a returned p-value, how would you decide whether the result is statistically significant?
Is a statistically significant p-value conclusive proof that the new routing algorithm is better? If not, what are the main threats to validity?
Propose a stronger experiment design and analysis plan for this routing algorithm.

“Felt okay about the pandas part but I second-guessed myself on why Welch specifically versus a standard t-test.”

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Waymo·Software Engineer·Technical Phone Screen

Apr 2026

Waymo SWE interview with a streaming algorithms problem that had a neat follow-up. The core question was more involved than it looked at first glance, and the extension to per-user tracking was a good test of whether you'd actually thought through the data structures.

Given a chronologically ordered stream of events where each event has a type and timestamp, define a 'streak' as a maximal run of the same event type where consecutive events are no more than max_gap apart in time. A 'Super Streak' is a streak with at least L events and a total duration of at least D. Count how many Super Streaks exist in the stream, solving it in a single forward pass.
Follow-up: each event now also carries a user_id. Extend your solution to count Super Streaks per user.

“I got the single-pass part right pretty quickly.”

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Waymo·Software Engineer·Onsite - System Design / Architecture

Apr 2026

System design round at Waymo for a software engineer role. The main problem was elevator scheduling, which started reasonable enough but got hairy when the interviewer pushed into high-rise territory. Felt okay leaving but not great.

Design the operation logic for an elevator system, including request scheduling and multi-elevator allocation.
How would you partition floors across elevators in a high-rise building? What changes about your design at scale?

“The core part went fine.”

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Waymo·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Waymo ML Engineer interview that leaned heavily into GPU/systems territory. The questions were around kernel optimization, memory layout, and fusion techniques, which felt more like a systems programming screen than a typical ML role.

How would you approach optimizing a compute kernel for performance on modern hardware?
What memory layout considerations matter when designing data structures for GPU workloads?
Explain kernel fusion and when you would or wouldn't apply it.

“Talked through memory access patterns, occupancy, and reducing warp divergence.”

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Waymo·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026

Fourth round at Waymo for a software engineering role, focused on technical leadership. Spent a bunch of time prepping project stories and got blindsided by a hypothetical scenario instead.

Given a hypothetical engineering leadership scenario, how would you handle it?

“Completely prepared for the wrong thing.”

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Waymo·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Waymo ML Engineer interview had me coding up logistic regression from scratch using only NumPy, plus a conceptual discussion on optimization and loss functions. Pretty deep for a single session, and the theory follow-ups caught me more off guard than the coding part did.

Implement binary logistic regression from scratch using only NumPy, including the forward pass, loss computation, gradient derivation, and a fit method with gradient descent.
Why do we use binary cross-entropy loss for classification instead of mean squared error?
How sensitive is logistic regression to the choice of learning rate, and how do you know when it has converged?
How would you implement a numerically stable version of the sigmoid function?

“I started with sigmoid and cross-entropy, which was fine.”

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Waymo·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Waymo ML engineer interview had me implementing K-Means from scratch using only NumPy, then defending design choices out loud. Pretty deep for a single coding round, they clearly wanted someone who actually understands the math and not just sklearn wrappers.

Implement K-Means clustering from scratch using only NumPy. Your solution should include a fit method that initializes centroids, iteratively assigns points to the nearest centroid, and recomputes centroids until convergence or a maximum iteration limit. Also implement a predict method for new points. Use vectorized broadcasting for distance computation, handle empty clusters, and report final inertia.
What is the computational complexity per iteration of K-Means, and how does initialization strategy affect the algorithm's sensitivity and final result quality?
How would you choose the right value of k? Walk through the elbow method and silhouette scoring.

“The vectorized distance matrix part is where I almost tripped up.”

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Waymo·Machine Learning Engineer·Recruiter / HR Screen

Apr 2026

Pretty standard recruiter screen for an ML Engineer role at Waymo. Nothing technical, just the usual background questions and resume walkthrough. Short and painless.

Why are you looking to leave your current role?
What are you looking for in your next position in terms of scope, team, tech stack, and growth?
Walk me through your resume, with a focus on your most relevant experience including deep learning work.

“Pretty routine.”

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Waymo·Software Engineer·Onsite - System Design / Architecture

Apr 2026

Waymo system design round for a software engineer role. The prompt was open-ended and tied to your own domain, which sounds flexible but actually made it harder to know where to anchor the discussion.

Design a system relevant to your own domain or area of expertise.

“The open-ended framing tripped me up more than a concrete prompt would have.”

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Waymo·Software Engineer·Onsite - System Design / Architecture

Apr 2026

Third round at Waymo for a software engineering role, focused entirely on data system design. The session was more nuanced than a typical design round because the interviewer kept pushing into the data imbalance problem between simulation and real-world testing.

Design a system to collect simulation logs and aggregate the results across large-scale runs.
How would you address the imbalance between having a huge volume of simulation data versus very limited real on-vehicle test data?

“This felt scoped enough that I had a decent starting point, pipeline ingestion, some kind of distributed aggregation layer, storage with queryability.”

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