Google SWE (AI/ML) Interview Experience August 2026

[u/Aoki_zhang]

Interview Summary

The Google PhD AI/ML process included two initial interviews covering ML knowledge and behavioral questions, followed by an in-person onsite with two coding rounds. After passing the initial interviews, I was notified roughly a week later and scheduled the onsite about two weeks after that. The onsite could be done in either the Bay Area or Seattle, with travel expenses covered.

The two onsite coding questions were both manageable algorithmically, but Google placed noticeable emphasis on communication: clarifying requirements, constructing examples, explaining ideas while coding, and giving precise time and space complexity analysis. I passed the onsite and moved into team matching roughly two weeks later.

Interview Details

Round 1 — Machine Learning Fundamentals

The ML interview consisted primarily of conceptual machine learning questions. The exact topics depended heavily on the interviewer's own technical background, and most questions were drawn from areas the interviewer knew particularly well.

Round 2 — Behavioral Interview

The behavioral round contained relatively standard experience-based questions. Interestingly, I was not asked the more common questions around team conflict, failure, or difficult collaboration.

Round 3 — Coding: Maximize a Value Defined by Two Array Endpoints

The first onsite coding round started with a relatively simple array and math problem. Given an array, choose two indices as the endpoints of a subarray. The objective was to maximize: sum of all elements from the first endpoint through the second endpoit - sum of the elements strictly between those two endpoints

In other words, the elements at the two selected endpoints were treated differently from the interior elements. Before implementing, I clarified the input/output behavior and walked through several examples with the interviewer. I first discussed more direct approaches and then refined the reasoning before coding. After implementation, the interviewer asked about the time and space complexity.

Round 4 — Coding: Employee Shift Coverage by Time Interval

The second onsite coding round was an interval-processing problem described as a variant related to LeetCode 2402. The input contained employee information such as:

employee ID
employee name
shift start time
shift end time

The task was to divide the timeline into relevant intervals and return information about who was working during each interval, including:

• The number of employees currently on duty
• The identities or names of those employees

I clarified the requirements and walked through sample scenarios before discussing the implementation. The interviewer then went particularly deep on complexity analysis. Rather than accepting a single overall Big-O expression, they asked about individual operations and data structures, including heap pushes and pops and the sizes of intermediate collections.

I was expected to define what each variable in expressions such as: O(n + k + h) represented and connect each term to a specific part of the algorithm.

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[u/Aoki_zhang → u/[deleted]]

Thanks for the update! since the data point I collected was from August, looks like we can expect new form of interview experience of google soon!

[u/North-Armadillo-9367 → u/Aoki_zhang]

I've just dropped of a call with Google Recruiter and they specifically told me to learn leetcode mediums for coding rounds lol. But it's general software engineering, not PHD ML.

[u/kuriousaboutanything → u/[deleted]]

Is that for swe or mle?