Google·Machine Learning Engineer·Technical Phone Screen
May 2026
Google ML Engineer interview that mixed a resume walkthrough with some surprisingly deep NLP theory and a mini product case. The tokenization section caught me a bit off guard with how far they wanted to go, and the LLM recommendation system case was more open-ended than I expected.
- Walk me through the NLP or ML projects on your resume. What were the key decisions you made and why?
- Compare word-level, subword (BPE), WordPiece, SentencePiece, and character-level tokenization. What are the tradeoffs and when would you use each?
- How would you design a recommendation system using an LLM? Walk through retrieval, prompt design, ranking, evaluation, and how you'd handle cold-start users.
“This part felt fine until they started poking at specific choices I'd made, like why I picked one architecture over another.” The rest of the author's notes on Machine Learning Engineer interview at Google, 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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