Google AI Engineer Interview Questions and How to Prepare (2026)

Independent preparation guide by Valenx Press. Not affiliated with or endorsed by the company named. Interview stages below are a general industry pattern; the linked public careers pages are the only company sources used.

Quick answer

Google does not publish the questions it asks, so treat any "Google AI engineer interview questions" list as practice material. Start from the live job post and Google's own hiring and interview prep pages, then prepare four things: coding in Python with clean tests and complexity, ML and LLM fundamentals, an ML or LLM system design with evaluation and cost, and two project stories with measured results. Practice the 10 original questions below out loud and score them with the free self-check.

What the public careers pages emphasize

Google publishes official pages on its hiring process and interview preparation, and its job search lists AI and ML openings across many teams. This guide uses only those public pages as company sources. It does not describe any team's actual questions or requirements.

Public careers sources: >Google Careers: How we hire | >Google Careers: Interview prep | >Google Careers job search

Titles vary. Search for software engineer roles with machine learning or generative AI in the description, applied AI roles, customer-facing AI engineering roles in cloud teams, and research engineering roles. Each one weights coding, modeling and product work differently. Read the qualifications list of the exact posting and note which skills appear in both the minimum and preferred sections, because those are usually the safest focus for your prep. If you are aiming at research teams, also read the Google DeepMind interview guide on this site.

Typical loop stages

The following is a general AI engineering industry pattern, not a verified Google interview schedule. Stages vary by team, seniority, location, and current opening. Use it to structure preparation and ask the recruiter which stages actually apply.

1. Recruiter conversation: role fit, level, location and timeline. Ask which rounds apply and whether any round focuses on ML.
2. Technical screen: usually a coding problem with discussion of correctness, edge cases and complexity.
3. Coding rounds: data structures and algorithms in a language you choose, with an emphasis on clear reasoning.
4. ML or LLM depth: fundamentals, model trade-offs and evaluation, tailored to the role.
5. System design: for experienced candidates, a design such as a recommendation, search or LLM-powered feature.
6. Behavioral: collaboration, handling ambiguity and learning from mistakes.

Round-by-round prep table

Round	What to practice	Free resource on this site
Coding	Arrays, graphs, dynamic programming, tests and complexity out loud	Timed mock interview round
ML fundamentals	Transformers, attention cost, scaling, overfitting and evaluation	Transformer architecture deep dive
LLM application	Retrieval, tool calling, structured output, safety	RAG interview questions
Evaluation	Offline sets, online A/B tests, LLM judges, regressions	LLM evaluation interview questions
System design	Requirements, data flow, serving, latency and cost	LLM API cost calculator
Behavioral	Two project stories with numbers and one honest failure	Answer self-check

A focused preparation plan

Spend the first third of your time on coding, because a weak coding round is hard to offset with ML depth. Solve problems out loud, state complexity, and write tests for edge cases before you are asked. In the second third, review ML and LLM fundamentals until you can explain each in two minutes: attention and its cost, why a model hallucinates, how quantization trades accuracy for speed, and when retrieval beats fine-tuning. In the last third, practice two designs end to end and rehearse your project stories.

Build one small project you can explain in depth, such as a retrieval assistant over a public dataset with a labeled evaluation set. Record the baseline, the change you made, the metric that moved and the one that regressed. Then use the study plan tool to schedule the work up to your interview date, and keep the >free Top 30 AI engineer questions PDF for review.

10 practice questions

practice questions, not leaked questions These are original, general AI engineer exercises, not claims about questions used by Google.

1. Coding: Return the k most frequent words from a stream, then discuss memory if the stream does not fit on one machine.

Scoring points: - Correct heap or bucket approach with complexity stated. - Handles ties, empty input and very large k. - Sketches a distributed count with a merge step for the follow-up.

2. LLM fundamentals: Why does attention cost grow with context length, and what can you do about it?

Scoring points: - Explains quadratic attention in sequence length and the memory growth of the KV cache. - Names practical mitigations such as caching, shorter prompts, retrieval and efficient attention variants. - Links each mitigation to a latency or quality trade-off.

3. RAG: Design question answering over a large set of internal documents with permissions.

Scoring points: - Chunking, hybrid retrieval and re-ranking with reasons. - Permission filters applied before ranking, not after generation. - Evaluation that separates retrieval recall from answer groundedness.

4. Evaluation: A new model version scores higher offline. How do you decide whether to launch it?

Scoring points: - Checks the evaluation set for coverage and contamination. - Plans an online experiment with guardrail metrics. - Defines rollback criteria before launch.

5. System design: Design a feature that summarizes long email threads for millions of users.

Scoring points: - Clarifies latency, privacy and cost constraints first. - Covers batching, caching, model size choice and fallbacks. - Measures quality with human ratings and user signals.

6. Fundamentals: When would you choose quantization, distillation or a smaller model?

Scoring points: - Defines each and its quality risk. - Ties the choice to a latency or cost target. - Validates on the task, not only on a generic benchmark.

7. Agents: How would you make a tool-using assistant safe enough to take actions for a user?

Scoring points: - Least privilege tools and confirmation for irreversible actions. - Handles prompt injection from tool outputs. - Logs every step and evaluates task success plus unsafe action rate.

8. Experimentation: How would you A/B test an AI feature whose output varies on every call?

Scoring points: - Randomizes by user and chooses metrics that capture value and harm. - Accounts for novelty effects and variance. - Pairs online results with offline evaluation.

9. Debugging: Users report answers got worse last week. Walk through your investigation.

Scoring points: - Checks recent changes: model, prompt, data, index and traffic mix. - Reproduces with logged examples and compares versions. - Adds a regression test once fixed.

10. Behavioral: Tell me about a time you changed your approach because of data.

Scoring points: - Specific situation, your own decision and the evidence. - Shows how you communicated the change. - States the result and what you learned.

FAQ

Does Google publish its AI engineer interview questions?

No. Google publishes general hiring and interview prep pages, but not the questions. Lists online, including this one, are practice material.

Is the Google AI engineer interview different from the software engineer interview?

It depends on the posting. Many ML-related roles keep a strong coding bar and add ML or LLM depth and design. Ask your recruiter which rounds apply to your opening.

How long should I prepare?

As general guidance, many candidates plan four to eight weeks, with more time on coding if algorithm practice is rusty. Use the study plan tool to fit the topics to your actual date.

Should I study Gemini specifically?

Know the public basics of large multimodal models and be ready to discuss trade-offs in general terms. Avoid claims about internal details you cannot support.

How is this page different from the Google DeepMind guide?

This page targets AI and ML engineering roles across Google product and cloud teams. The DeepMind guide focuses on research engineering and research-adjacent roles.

All company interview guides · Prep guides · Question deep dives