Waymo·Machine Learning Engineer·Technical Phone Screen
May 2026
Waymo ML engineer interview that was basically a long open-ended data engineering conversation. No coding, no LeetCode, just them handing you ambiguity and watching how you swim. Felt more like a system design session than anything else, and I was not fully prepared for how much they expected me to drive it.
- How would you approach building a pipeline to process a CSV file and prepare it for downstream engineering teams, given that you don't know the downstream requirements upfront?
- Given a single sample row as a raw string, how would you infer a schema, assign types, and define validation rules for the data?
- What parsing considerations would you think through for a CSV file, covering things like delimiters, quoting, escaping, and encoding?
- How would you handle schema evolution over time, and what's your strategy for malformed or unparseable rows?
- How would you decide on an output format and delivery mechanism for the processed data, and how would you ensure pipelines can be re-run safely?
- How would you test this pipeline, monitor it in production, and communicate the data contract to downstream teams?
“This was the whole interview, basically.” The rest of the author's notes on Machine Learning Engineer interview at Waymo, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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