Tech Deep DiveOnsiteSoftware Engineer, Machine Learning EngineerLast reported April 2026Low Frequency
Problem Overview
Given two sets of experimental data from autonomous driving systems (e.g., two experimental groups each producing latency or performance metrics for a planning module or overall AV system), analyze and determine which solution/approach is better. The candidate must define and discuss key progress metrics for autonomous driving, then apply a structured analytical framework to compare the two datasets and justify a conclusion. The interviewer typically spends significant time explaining the autonomous driving technical stack and background before the actual question begins.
Follow-up Prompts
Interviewers escalate the problem with these extensions. Be prepared to discuss each one.
01How do you handle the fact that simulation can generate much more data than real on-road testing? How does that affect your analysis? (when: Candidate proposes a set of metrics or picks a winning experiment)
02Given two groups of latency results, how do you determine which configuration is definitively better? (when: Candidate completes statistical comparison of two latency groups)
Waymo Focus
**Alternative approaches:** Qualitative/domain-driven metric discussion (Easier to structure without deep stats knowledge; can demonstrate AV domain expertise, but may not satisfy a data-fluency interviewer who expects quantitative rigor.); A/B testing framework (Familiar structure for many data-oriented engineers; applicable when sample sizes are known, but may be harder to apply when simulation vs. real-world data imbalance is significant.)
Waymo · Tech Deep Dive · Reported 2× across candidate reports