Waymo Machine Learning Engineer interview questions

23 Machine Learning Engineers have shared notes from their Waymo interviews, with the questions they were asked in each round. That is 26% of the 87 Waymo interview notes on Screna AI.

23 Machine Learning Engineer interview notes

Waymo·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Waymo ML Engineer interview that came down to a classic string problem, but they pushed through all three solution tiers which made it feel more like a depth check than a coding warmup.
  • Given a string, find the longest palindromic substring. Walk through your approaches from brute force to optimal, and explain the trade-offs at each step.

“Started with the O(N^3) brute force just to show I wasn't going to skip steps, then moved to expand-around-center which got me to O(N^2) with constant space.”

View Post

Waymo·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Waymo ML engineer interview that went deep on model efficiency and training techniques. Four meaty topics back to back, each with a 'how would you evaluate this in production' follow-up that I was not fully ready for.
  • Explain quantization-aware training: what it is, why you'd use it, how it works, common pitfalls, and how you'd measure its impact on a production model.
  • Walk through knowledge distillation: the purpose, the mechanics, when it's the right tool, what can go wrong, and how you'd verify it actually helped in a real deployment.
  • What is evaluation mode in deep learning frameworks, why does it matter, when do people get it wrong, and how would you catch bugs related to it in a production pipeline?
  • Describe contrastive learning: the core idea, how it's trained, what scenarios it's useful for, what tends to go wrong, and how you'd evaluate whether it improved a production model.

“I knew the basics but fumbled on the pitfalls section.”

View Post

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

Jun 2026
Waymo ML engineer system design round, one big open-ended question about inference serving at massive scale. Felt like a gauntlet that kept expanding the more I answered.
  • Design a production inference serving system for an ML model that needs to support 100 million daily active users. Walk through traffic and QPS estimates, memory requirements for weights and caches, network and accelerator bandwidth, hardware selection, latency optimization, and OOM failure handling.

“This question sprawls in every direction and I underestimated how much ground it actually covers.”

View Post

Waymo·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Waymo for an ML engineer role, pretty much one question about mentorship and that was the bulk of it.
  • Tell me about a time you mentored other engineers.

“I had a story ready but fumbled the structure a bit.”

View Post

Waymo·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Waymo ML engineer interview had a heavy emphasis on implementing algorithms from scratch, no libraries allowed. You really need to know the math cold, not just the API calls.
  • Implement linear regression from scratch using gradient descent. Walk through the cost function, derive the gradient update, and compare the iterative approach to the closed-form solution.
  • Build a K-nearest neighbors classifier from scratch. What data structures would you use, how do you implement fit and predict, and what's the time complexity?
  • Explain how you'd implement a decision tree split using information gain. How does the algorithm decide which feature to split on?
  • Implement a Naive Bayes classifier from scratch. Cover the data structures, the fit and predict methods, and how you'd handle zero probabilities.

“This is where I spent most of my prep and it still felt shaky in the moment.”

View Post

Waymo·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Waymo ML Engineer interview had me coding a neural network from scratch in NumPy, no frameworks allowed. Pretty brutal if your backprop math is rusty, but also kind of a fun problem once you get into it.
  • Implement a two-layer feed-forward neural network from scratch using only NumPy, including forward pass, backward pass with chain-rule gradients, and SGD parameter updates. Train it on a toy dataset and show the loss going down.
  • Walk through weight initialization strategies: why does initializing all weights to zero fail, and how does Xavier initialization help?
  • Why does ReLU help with the vanishing gradient problem compared to sigmoid or tanh?
  • How would you verify your manually coded gradients are correct using finite differences?

“This was the whole interview basically.”

View Post

Waymo·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Waymo ML Engineer interview, got a maze robot problem that looked like a graph question but turned out to be something weirder. Took me a while to even understand what was being asked.
  • Given a 2D maze grid where cells are walls, empty spaces, or a single exit, design an instruction sequence (using U/D/L/R moves) such that a robot with no knowledge of its starting position is guaranteed to reach the exit after executing the full sequence. The robot stays put if a move would hit a wall or go out of bounds.

“Spent the first few minutes just re-reading the problem because I kept thinking it was a shortest-path thing.”

View Post

Waymo·Machine Learning Engineer·Onsite - Coding / Algorithms

Jun 2026
Waymo ML engineer interview with a debugging-heavy coding round. They handed me a small tensor utility library with three distinct bugs and asked me to find them, fix them, and write tests. Not the typical leetcode grind, which I appreciated, but the aliasing bug in particular took me longer than I'd like to admit.
  • Given a buggy tensor utility library with issues in matrix initialization, array conversion, and chunk splitting, identify all the bugs, fix them, and add tests covering aliasing, shape correctness, and non-divisible chunk sizes.

“The aliasing one is sneaky.”

View Post

Waymo·Machine Learning Engineer·Technical Phone Screen

May 2026
Waymo ML engineer interview with a physics/spatial reasoning problem involving two colliding objects and a diagram. Pretty niche question, not your typical ML theory stuff.
  • Given two objects on a collision course, use the provided diagram to determine what the outcome of the collision will be.

“This one threw me a bit.”

View Post

Waymo·Machine Learning Engineer·Technical Phone Screen

May 2026
Waymo ML Engineer interview with a coding round focused on data structures. The problem was a trie implementation for prefix search, which sounds straightforward until you're also expected to walk through complexity tradeoffs on the spot.
  • Given a list of words, design a data structure that takes a prefix string and returns all words in the list that start with that prefix. Walk through your implementation and analyze the time complexity.

“I went with a trie, which felt like the obvious move.”

View Post