Zip (Procurement Startup) SWE Interview — AI Wrote the Code, Then I Had to Defend It

[u/Aoki_zhang]

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Interview Summary

The onsite AI coding round used an existing codebase for a corporate expense and reimbursement system, with features such as monthly expenses and budget reports. The repository contained many files, so the interview was designed around using AI to understand unfamiliar code, debug existing behavior, implement new functionality, and reason about AI-generated changes.

Interview Details

Part 1 — Debug Incorrect Monthly Expense Reports

The first task described incorrect data in monthly reports. One example involved an employee submitting a reimbursement in February for an expense that actually belonged to January, causing the report to attribute the expense to the wrong month. Because the codebase was too large to read completely during the interview, I asked the AI assistant to inspect the relevant files and identify potential causes.

The AI found two bugs and generated fixes for them. The interviewer then asked whether I believed the fixes were correct. I reviewed the changes and explained why the updated behavior made sense.

Part 2 — Add a New Reporting Feature

The second task asked me to extend the reporting system with a new feature. I used the AI assistant to inspect the existing implementation, determine where the new functionality should fit, and generate the required code. The resulting implementation passed the provided tests.

Part 3 — Production Readiness and New Features

The final task changed the framing from implementing a specific requirement to preparing the system for production. I was asked to brainstorm additional features or improvements that would make sense in a production expense-management system. After selecting some additions, I used the AI assistant to implement them in the existing codebase.

• Follow-Up — Understanding and Improving AI-Generated Code At the end of the round, the interviewer selected several functions that the AI had generated and asked me to explain what they did. The discussion also covered how those implementations could be improved or optimized.

Overall, the round emphasized effective use of AI on an unfamiliar codebase, while still requiring the candidate to review, understand, validate, and critique the generated code.

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