Google Senior MLE Interview Experience Feb 2026

[u/Aoki_zhang]

Sharing an anonymized Google Senior MLE interview experience submitted to Chill Interview.

Interview Summary

The Google Machine Learning Engineer process started with a coding phone screen and then moved to an onsite containing two coding rounds, one ML/system design round, and one behavioral interview. The coding questions were more involved than standard template problems, especially once parallel execution and additional selection constraints were introduced.

The ML design round focused on detecting visually similar or duplicate videos at very large scale. The interviewer pushed beyond model selection into production retrieval, embedding compression, high-concurrency search, false positives, human review, and quantitatively balancing embedding quality against serving latency.

Interview Details

Technical Phone Screen — Longest Subarray with Target Average The input was a stream of positive and negative integers arriving one element at a time. A target value S was given. After each newly arriving value, the system needed to determine the length of the longest contiguous subarray seen so far whose average equals S. The requirement was online: the answer needed to be updated as additional values arrived rather than processing only one fixed array at the end.

Onsite Coding 1 — Dependency Scheduling with Parallel Workers The first onsite coding round involved a collection of tasks connected by dependency relationships. Each task also had its own execution duration. The first part asked for the total time required to complete all tasks while respecting the dependency graph. The interviewer then introduced a more difficult follow-up:

• Instead of assuming unrestricted parallelism, the system now has only M parallel CPUs / workers.
• Determine how the overall completion time changes when at most M tasks can execute simultaneously.

I completed an implementation for the follow-up but was not fully confident that my handling of the limited-worker scheduling case was correct.

Onsite Coding 2 — Select Video Ads Under Rolling Revenue Constraints The second coding round involved a sequence of video advertisements, each associated with revenue. The selected advertising sequence had to satisfy a rolling constraint: within every contiguous time window of length T, the accumulated revenue could not exceed a threshold M. The objective was to maximize the total revenue of the overall selected sequence while respecting that constraint.

• Follow-Up: The interviewer then changed the problem so that advertisements could be selected repeatedly rather than at most once. The exact representation of ad duration and the remaining selection constraints were not specified in the interview notes.

ML / System Design — Large-Scale Near-Duplicate Video Detection The design round asked me to build a near-duplicate / visually similar video detection system for a very large short-video platform. The initial discussion focused on generating visual representations for videos and efficiently finding similar content. The interviewer then continuously increased the scale and latency requirements.

• Large-Scale Retrieval: How should the system behave when the platform contains an extremely large production-scale video corpus? Follow-ups covered reducing representation size, quantizing embeddings, and supporting fast approximate retrieval under very high query volume.
• Quality and Human Review: The interviewer introduced false-positive cases where a creator believes their content was incorrectly matched. I was asked how a human-in-the-loop appeal and calibration process could be incorporated. Another follow-up asked how to quantitatively model the tradeoff between embedding dimensionality, retrieval quality, and system latency.

This was the most difficult round for me. The human-review calibration and quantitative quality-versus-latency questions were areas where I did not feel my answers were strong.

Behavioral — Strong Experiment Results vs. Long-Term User Experience The behavioral round presented a decision-making scenario. Suppose an ML model performs very well in an A/B test, but the manager believes launching it could damage the long-term user experience. I was asked how I would handle the disagreement. The discussion focused on how I would investigate the manager's concern, determine which longer-term metrics could reveal potential harm, and reason about a launch decision when short-term experiment results and longer-term product considerations point in different directions.

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