Updated by Waymo candidates
Machine Learning Engineer Interview Experience
I was definitely caught off guard because I was expecting more of a LeetCode type of question, and instead they showed a diagram about obstacles colliding and wanted me to figure out which one was bigger and where the force would move.
Interview process
I applied directly on the website with no referral and went through a pretty Google-like process. The loop was a basic recruiter chat, then a surprisingly hard LeetCode-style coding screen, then a final loop with another coding round, an ML coding round, an ML system design round, and two behavioral rounds. What really stood out was that the coding bar felt basically software-engineer level, not a watered-down MLE bar, and then on top of that they still expected solid ML depth. The strangest round for me was the final coding interview because it was more practical and diagram-based than I expected, while the behavioral rounds went deeper into the technical details of my projects than most companies do. Do not under-prepare on DSA just because the title says machine learning.
- Recruiter screen
- Technical interview
- Final round
Interview tips
I'd tell a friend to brush up hard on data structures and algorithms before anything else, because if you underprepare there you might not even get past the first screen. Also, always clarify what they mean by a coding round, because sometimes they say coding and mean pure LeetCode, and other times they mean ML coding with NumPy, data manipulation, or even model-related implementation. For behavioral, write down your stories and pick one project that has both real technical depth and cross-team complexity. For ML system design, go in with a framework, but keep your ears open because their follow-ups will tell you where they want depth.
Company culture
My impression is that Waymo is running a pretty standardized process right now. I was told London and Silicon Valley use the same loop, and senior and staff go through the same interviews with leveling decided afterward. The interviewers felt very Google-like to me: not much handholding in coding, one harder question instead of multiple easier ones, and a strong emphasis on whether I could actually land a solid solution. Even the behavioral rounds felt more technical than average, which told me they want MLEs who can code like SWEs, talk deeply about ML systems, and also show leadership on large projects.
Questions asked
Overview
One of the final-loop coding rounds felt much more practical and diagram-driven than normal LeetCode, and it caught me off guard.
Question types asked
Specific questions asked
Given this collision diagram, figure out which of the two objects is bigger and which direction the resulting mass will move, then code it.
Can you first explain how the collision is happening from the diagram before you start coding?
They showed a diagram with two objects colliding and I had to reason through who was bigger and which direction the resulting mass would move, then code it. I spent more time than I wanted just understanding the prompt, because I was expecting a cleaner LeetCode problem and this felt much closer to the kind of problems they'd actually face. My read was that it mixed stack and tree/graph-style thinking. I got through it, but this was probably my weakest round because I lost too much time clarifying.
Get full access with a membership, or share your experience to try it free.
