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.
$139K
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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Real interview reports from people who went through the Waymo process.
The process moved pretty quickly for me once the recruiter reached out. The recruiter was friendly and accommodating, and everything was scheduled within about two weeks. I ended up with two interviews in the same day: one coding round and one ML design round. The coding portion was LeetCode-style and, in my case, leaned into graph questions rather than anything overly exotic. It felt manageable if you’re already in good shape for standard FAANG-style prep, which is basically how I’d describe the overall difficulty too — not trivial, but not brutal.
The ML design interview was more about fundamentals than a super specialized system design deep dive. I was asked to walk through core ML concepts and domain knowledge, and there was also a straightforward behavioral question about why I wanted to work there. In another round, they asked me to talk about my experience, so it helped to be ready to summarize past projects clearly and connect them back to the role. The interviewer was kind and the conversation felt smooth, which took some of the pressure off. I didn’t make it through the process, but it was a pretty fair interview loop overall. If I were doing it again, I’d focus on graph problems for coding and make sure I could explain ML fundamentals and my own background crisply without rambling.
Prep tip from this candidate
Brush up on LeetCode graph problems and be ready to explain core ML concepts and your past experience clearly. The ML design round sounded more fundamentals-focused than highly specialized, so practice concise answers to “why this company” and project walkthrough questions.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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 | |
|---|---|
| Location Frequency | |
| 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 | |
| Complete Addresses | |
| Hurdles In Data Projects | |
| Find the First Non-Repeating Character in a String | |
| Median Probability | |
| RMS Error | |
| Reducing Error Margin | |
| Lasso vs Ridge |
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.