Candidates describe Google AI Engineer interviews as mixing coding, technical role-related knowledge (RRK) questions, and design discussion. This guide covers the reported formats, sample questions and how to prepare.
$171K
Avg. Base Comp
$260K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describe Google's AI Engineer interviews as a blend of conventional coding and deeper role-specific questioning, and the reports vary by team and level. One candidate described a 60-minute coding interview run in a Google Doc with no IDE. Another reported a LeetCode medium-to-hard problem where a working brute-force solution was not enough. The interviewer asked for optimization, and the feedback placed the performance at L4 rather than L5. A candidate for a forward-deployed role was given an outline covering complexity analysis, sorting, hash tables, heaps and trees. Another worried the coding could be either ML-focused or general algorithms depending on the interviewer. Practice both, and practice explaining your reasoning aloud without an IDE.
The Role Related Knowledge (RRK) round is where the reports get specific. One candidate was asked to reason through the central limit theorem and the binomial distribution to size a confidence interval. They had to keep justifying each step rather than staying at a high level. A Staff-level candidate was told to expect ad hoc technical questions and design discussion rather than behavioral questions. Another was preparing to discuss the technical decisions and depth of the AI projects on their resume.
Expect to frame technical choices in terms of customer impact. One forward-deployed-style candidate was told they were not technical enough to face customers. Another found that an introductory call with the hiring manager turned into an interview with situational questions about an AI product's challenges. Confirm whether a call is evaluative, and clarify context and constraints before proposing a solution. One candidate was told the full loop needed to finish within roughly two weeks, so start preparing early. Only some candidates had finished their loop, so outcomes are still limited.
Synthesized from 10 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Google process.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Google
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| 2nd Highest Salary | |
| Top Three Salaries | |
| First Touch Attribution | |
| First to Six | |
| Merge Sorted Lists | |
| String Shift | |
| 500 Cards | |
| Size of Joins | |
| Last Transaction | |
| Top 3 Users | |
| Raining in Seattle | |
| Third Purchase | |
| Job Recommendation | |
| Minimum Change | |
| Impression Reach | |
| Lazy Raters | |
| Jars and Coins | |
| Longest Increasing Subsequence | |
| Find the First Non-Repeating Character in a String | |
| Daily Retention Summary | |
| Complete Addresses | |
| Bucket Test Scores | |
| RMS Error | |
| Hurdles In Data Projects | |
| Find the Index with Equal Left and Right Sum | |
| Reducing Error Margin | |
| Find Bigrams | |
| Detecting ECG Tachycardia Runs | |
| LLM Enterprise Search |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter or hiring-manager conversation. One candidate passed an initial screening before the coding and RRK interviews. Another found that an intro call with the hiring manager turned into an evaluative interview with situational AI product questions. Ask what format to expect.
One candidate describes a 60-minute coding interview in a Google Doc. Another reported a medium-to-hard problem where optimizing beyond brute force was expected. A forward-deployed candidate was given an outline covering complexity analysis, sorting, hash tables, heaps and trees. Practice explaining your reasoning aloud without an IDE.
Candidates describe RRK as role-specific technical questioning rather than behavioral discussion. One reported statistics questions using the central limit theorem and binomial distribution to size a confidence interval. Interviewers may keep drilling into the reasoning behind each answer, so prepare to justify your choices rather than recite definitions.
One candidate was scheduled for two coding rounds and one design round in an in-person loop, with a virtual option available on request. Another was told to expect design discussion within RRK. No specific design prompts were reported, so practice discussing designs at a practical level.
Candidates report preparing to discuss the technical decisions and depth of their past AI projects. One forward-deployed-style candidate received feedback about customer-facing readiness. Prepare to explain the decisions behind your projects and how you would communicate a solution through ambiguous client problems.
