26 traps set in interviews at Google. Each one: the interviewer's question, the answer most candidates give, and the mechanism that breaks it.
Roles: Senior AI Engineer (6), Staff ML Engineer (6), Senior ML Engineer (5), Machine Learning Engineer (3), ML Engineer (2).
From RAG Interview Questions, LLM System Design Interview, Machine Learning System Design Interview, Advanced NLP Interview Questions, Computer Vision Interview Questions, LLM Inference Interview Questions, Advanced Reinforcement Learning Interview Questions and AI Agent Engineering Interview.
Each trap is set in an interview at Google. AI Interview Prep isn't affiliated with Google.
Senior AI Engineer interview at Google
“Your agent has 70 REST tools and needs 12 sequential calls to compare smartphone prices across 4 countries. Switching to code execution raises success rates and cuts turns. So why isn’t code execution the default for every agent?”
Agent Engineering #1
Sep 20, 2026
Staff ML Engineer interview at Google
“Your tokenizer gets 2.5 bytes/token on English but 1.1 on Vietnamese and Thai. Your teammate wants to grow the vocab from 100k to 250k. Do you ship it?”
LLM System Design #51
Aug 26, 2026
Staff AI Engineer interview at Google
“You shipped a code-executing agent behind an AST sandbox that blocks os, subprocess, and dunder access. Security signed off. Six weeks later you’re breached. What class of attack did your sandbox structurally fail to stop?”
LLM Inference #13
Aug 11, 2026
Senior AI Engineer interview at Google
“Your web agent uses set-of-marks, you screenshot the page, draw numbered boxes on every element, and let the VLM click by number. It works in your demo but in production the model keeps clicking box 41 when it meant box 14, and it ignores half the page. The VLM is state-of-the-art and multimodal. So why is it failing?”
LLM Inference #6
Aug 4, 2026
Senior ML Engineer interview at Google
“Your GraphRAG pipeline uses an LLM to extract entity-relationship triplets. It’s flawless in the demo. Now you’re ingesting 50K docs a week and your indexing bill is on fire. What did you trade away when you picked LLM extraction?”
RAG #21
Jul 25, 2026
Staff ML Engineer interview at Google
“Your REALM-style retriever learns from its own retrieval signal, no human labels. What breaks after six months in production?”
RAG #20
Jul 24, 2026
Senior ML Engineer interview at Google
“Your conversational RAG has five query rewrite strategies. Why not just run all five and concatenate the outputs?”
RAG #19
Jul 23, 2026
Senior ML Engineer interview at Google
“Adaptive RAG routes queries by predicted hop count, but nobody labels this question needs 2 hops. The paper bootstraps silver labels by brute-forcing every hop count and keeping whichever one got the right answer. What’s the ceiling that creates?”
RAG #18
Jul 22, 2026
Senior AI Engineer interview at Google
“Your RAG pipeline scores 90% on factual lookups but collapses on ‘summarize this 300-page report.’ Why, and don’t tell me it’s chunk size?”
RAG #13
Jul 17, 2026
Staff ML Engineer interview at Google
“You and a teammate both ship IVF+PQ (Inverted File and Product Quantization) with identical memory budgets. Their recall is noticeably higher than yours. You quantized the raw vectors. They quantized the residual. Why does that one change beat you?”
RAG #10
Jul 14, 2026
Staff ML Engineer interview at Google
“Your RAG system hits 99% recall on HNSW in the demo. Now it’s 200M vectors and your RAM bill just triggered a budget review. Do you keep HNSW? Why or why not?”
RAG #8
Jul 12, 2026
Senior AI Engineer interview at Google
“Your RAG system pulls from a SQL database, a private company corpus, and web search. The answers now contradict each other. How do you fix source selection?”
RAG #7
Jul 11, 2026
Search Infrastructure Engineer interview at Google
“We benchmarked dense retrieval against BM25 on our eval set. Dense won on every metric. Do we rip out sparse retrieval entirely?”
RAG #5
Jul 9, 2026
Staff ML Engineer interview at Google
“We want to swap our BM25 + dense pipeline for generative retrieval. Our corpus gains 50,000 documents every Monday. Why might that be a dealbreaker?”
RAG #4
Jul 8, 2026
Staff ML Engineer interview at Google
“Your RAG benchmark shows semantic chunking beating fixed-size by 8 points. You ship it. Production retrieval quality doesn’t move an inch. What happened?”
RAG #2
Jul 6, 2026
Senior ML Engineer interview at Google
“You’ve got three retrievers, BM25, a dense embedding model, and a rerank pass, and their relevance scores live on completely different scales. How do you merge them into one ranked list?”
RAG #1
Jul 5, 2026
Senior AI Engineer interview at Google
“If the discrete token bottleneck is the main reason we need RLHF, why not just use the Gumbel-Softmax trick to make sampling differentiable and backpropagate end-to-end?”
Reinforcement Learning #18
Feb 13, 2026
Senior Computer Vision Engineer interview at Google
“We’re building a similarity search for a new dataset. If I arbitrarily rotate the feature space by 45 degrees, which distance metric falls apart: 𝐋1 𝐨𝐫 𝐋2? And what does that tell you about our feature engineering strategy?”
Computer Vision #4
Jan 5, 2026
Senior Computer Vision Engineer interview at Google
“We trained a high-capacity ResNet on 500k images, but it’s still overfitting. My Product Manager wants to spend $20k to label another 500k random images scraped from the same source. Do you approve the budget?”
Computer Vision #2
Jan 3, 2026
Senior ML Engineer interview at Google
“We’re building a toxicity detection dataset where only 1% of comments are actually toxic. We hired two annotators. Their inter-annotator agreement is 99%. Are we good to go?”
NLP #22
Dec 27, 2025
Senior AI Engineer interview at Google
“We ran an eval on a fixed dataset. Llama3 achieved a perplexity of 2.1, while Gemma3 scored 2.4. Which model is the better probability estimator, and which one do we deploy?”
NLP #18
Dec 24, 2025
Machine Learning Engineer interview at Google
“We have 3 upstream microservices generating features. They write to a central 𝘗𝘰𝘴𝘵𝘨𝘳𝘦𝘴 𝘋𝘉. Your ML Service queries that DB to get the input vector for inference. How do we scale this to 50k requests per second?”
ML System Design #19
Dec 5, 2025
Machine Learning Engineer interview at Google
“Our edge model is vulnerable to adversarial noise, but we have strict latency limits. Should we avoid quantization (keeping Float32) to preserve model stability?”
ML System Design #15
Dec 2, 2025
ML System Design #11
Nov 29, 2025
ML Engineer interview at Google
“We need to serve our model for two different use cases: a low-latency chatbot that needs a fast 𝐓𝐢𝐦𝐞-𝐭𝐨-𝐅𝐢𝐫𝐬𝐭-𝐓𝐨𝐤𝐞𝐧 (𝐓𝐓𝐅𝐓), and a high-throughput batch summarization job. How do these two workloads stress the GPU differently, and what fundamental tradeoff are you managing?”
LLM System Design #14
Nov 11, 2025
ML Engineer interview at Google
“Your team is struggling with training instability and exploding gradients in a new 100B+ model. The original ‘Attention Is All You Need’ paper used post-norm with learning rate warm-up. Why is that a bad idea for deep models, and what’s the one simple architectural change that solves this?”
LLM System Design #4
Nov 5, 2025