Let’s talk about the overall feeling first: Waymo Research Intern interviews are not low intensity, but the direction is very clear. The whole process does not pursue fancy question types, nor does it test the speed of answering questions. The core is one thing - whether you have really done research and can explain the research clearly. The following is a review of the entire process in rounds for reference only.
Waymo Research Intern interview process overview
Round 1: Recruiter Screen (approximately 30 minutes)
Round 2: Research Deep Dive
Round 3: ML + Math Basics
Round 4: Applied Research Case
Round 1: Recruiter Screen (approximately 30 minutes)
The pace is very fast, mainly doing background and matching confirmation. I won’t dig deep into technology, but I will repeatedly confirm whether what you are doing is more research than engineering or pure implementation.
Frequently asked questions include:
What is your current research direction that you are most familiar with?
Among the projects you have done before, which one do you have the most say in?
Does the graduation date & internship length comply with the team’s arrangements?
Round 2: Research Deep Dive
This is the most important round of the entire interview. The interviewer will ask you to talk about a research project you are most familiar with in full, from problem definition to experiments and conclusions. There will be almost no interruptions in the middle, but the questions are very dense.
Points that were highlighted include:
Why this question is worth doing
How was baseline chosen?
Why is metric reasonable?
Have you done ablation?
How did you adjust your assumptions when the results were not ideal?
Halfway through, I was asked a very typical question: If the data distribution changes, can this method still work? How would you verify the stability of the model?
Round 3: ML + Mathematical Fundamentals
This round is more basic ability verification, but the difficulty is not low, and the questions are basically combined with autonomous driving scenarios.
Those who made a deep impression include:
How to understand bias / variance trade-off in perceptual models
How do you design loss when label noise is heavy?
We won’t test any side tricks, but we do require you to explain the principles clearly and be able to apply them to specific scenarios.
Round 4: Applied Research Case
This round is more focused on “research decisions in real systems.” We will give you a research scenario related to autonomous driving, allowing you to dismantle your ideas on the spot, such as:
Which failure cases will make you reject this model directly? If inference latency becomes higher but accuracy improves, how will you weigh it?
What really stumps people is not the questions, but the research logic
During our long-term interviews with research positions at Waymo/Google/Meta, etc., we found that many candidates were not insufficiently capable, but rather unstable in actual practice. If you are preparing for Waymo Research Intern, or have already received an interview but are unsure, you can contact us directly for targeted interviews. Interview assistance .
