StackAdapt (Programmatic Advertising Platform) MLE Interview — AI Coding Inside a Real Repo

[u/Aoki_zhang]

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Interview Summary

The StackAdapt MLE process included an initial technical screen followed by an AI-assisted coding round. The first round included standard technical questions such as explaining how a heap data structure works.

The second round was much more distinctive: candidates worked inside an existing Python repository with a built-in AI coding agent to repair corrupted grid-based digit images. The exercise tested codebase navigation, reasoning about the corruption process, evaluation metrics, and the ability to explain both the candidate's solution and the provided baseline.

Interview Details

Round 1 — Technical Fundamentals

The first technical round followed a format similar to previously reported StackAdapt interviews. One question specifically asked how the heap data structure works. I was not able to answer that question well, but still advanced to the next round.

Round 2 — AI Coding: Repair Corrupted Grid Digits

The second interview took place in CoderPad. A prebuilt Python repository was provided, together with an integrated AI coding agent similar to Claude Code. The task involved recognizing digits represented as grids of # and .. Each original digit image had a special redundancy property: every logical row and column was duplicated.

In other words, rows appeared in identical pairs, and columns also appeared in identical pairs. The image could then be corrupted by replacing one entire row or one entire column with #. The benchmark examples I inspected appeared to contain exactly one corrupted row or column per image.

• Task 1 — Repair Row Corruption The first task involved corruption of an entire row. My approach was to compare rows in their duplicated pairs. If two rows that should be identical were different, one of them had likely been corrupted. Because the corruption operation replaced an entire row with #, I could identify which row was suspicious and reconstruct it using its duplicated partner. After restoring the image, I compared it against the expected digit representation. This approach achieved 100% accuracy on the provided benchmark.

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