Waymo ML Engineer interview typically runs 2 rounds: coding and ML design. The process usually wraps in about two weeks and is fairly smooth and fast-moving.
$189K
Avg. Base Comp
$410K
Avg. Total Comp
2
Typical Rounds
2 weeks
Process Length
We’ve seen Waymo evaluate ML Engineer candidates with a very practical lens: can you solve standard technical problems cleanly, and can you explain the ML choices behind your work without drifting into buzzwords? One candidate described the coding as LeetCode-style with a noticeable graph-problem tilt, which suggests the team is looking for solid algorithmic fluency rather than niche tricks. The difficulty was described as fair and familiar, closer to what strong FAANG-style prep would cover than to anything unusually specialized.
On the ML side, the recurring theme is breadth over theatrics. Our candidates report that the design conversation stays rooted in core ML fundamentals and domain knowledge, not an abstract system-design marathon. That means the interviewers seem to care less about memorized architecture patterns and more about whether you can reason through model behavior, tradeoffs, and the basics of applied ML in a transportation context. We also see a clear emphasis on communication: candidates were asked to explain why they wanted to work there and to summarize past projects crisply.
The non-obvious make-or-break factor is how well you connect your background to the role in a way that feels direct and grounded. The strongest signal here is not a polished pitch, but a candidate who can talk through prior work clearly, answer fundamentals confidently, and show they understand the practical realities of building ML systems that matter in the real world.
Synthetized from 1 candidates reports by our editorial team.
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Featured question at Waymo
Design an end-to-end ML system for personalized car recommendations at Cars.com, supporting real-time and batch inference, data distribution shift detection, and CI/CD for frequent model updates.
| Question | |
|---|---|
| Pathfinder in Maze | |
| Shortest Path Algorithms | |
| Merge Sorted Lists | |
| String Shift | |
| First to Six | |
| Job Recommendation | |
| Find Bigrams | |
| 500 Cards | |
| Get Top N Frequent Words | |
| The Brackets Problem | |
| P-value to a Layman | |
| Raining in Seattle | |
| Nearest Common Ancestor | |
| Minimum Change | |
| Impression Reach | |
| Type-ahead Search | |
| Jars and Coins | |
| Basic Regex | |
| Lazy Raters | |
| Same Algorithm Different Success | |
| Bucket Test Scores | |
| Hurdles In Data Projects | |
| Complete Addresses | |
| Find the First Non-Repeating Character in a String | |
| Median Probability | |
| RMS Error | |
| Reducing Error Margin | |
| Lasso vs Ridge | |
| Friendship Timeline |
Synthesized from candidate reports. Individual experiences may vary.
A recruiter reaches out to start the process and coordinates scheduling. In this experience, the recruiter was friendly and accommodating, and the overall process moved quickly from first contact.
The interviews were scheduled relatively fast after the initial outreach, with everything arranged within roughly two weeks. This stage appears to be mostly logistics and timing rather than a substantive screening round.
One of the two interviews was a LeetCode-style coding round. The questions leaned toward graph problems and felt comparable to standard FAANG-style preparation, with difficulty described as manageable but not trivial.
The second interview focused on ML design, but it was more about fundamentals than a deeply specialized system design exercise. The interviewer asked about core ML concepts, domain knowledge, and a straightforward behavioral question about why the candidate wanted to work at Waymo.
The candidate was also asked to talk through past experience and projects, so being able to summarize work clearly mattered. The conversation emphasized connecting prior ML work back to the role and explaining background crisply without rambling.
After the same-day technical interviews and background discussion, the candidate did not receive an offer. The overall loop was described as fair and smooth, but there was no indication of additional onsite rounds beyond the two interviews completed that day.