AI Coding Interview w/ Meta Staff Engineer

Overview

A former Meta staff engineer demonstrates a mock AI coding interview using a “word container” problem: given a list of words, return each word that contains a different, shorter word from the same list as a substring. The exercise illustrates both how to solve the problem and how to use an AI assistant effectively during an interview.

Interview Structure and Code-Comprehension Approach

The host describes a typical AI coding interview as having three main phases.

1. Understand the codebase

• Start with the main function to see how the program runs and where its inputs come from.
• Identify key classes and functions, including how words are validated and how substring checks work.
• Use tests to understand the expected behavior.
• AI can speed up this phase: ask for concise summaries of key functions or brief comments describing what each function does. Still read the code and verify the summaries.

2. Find and fix bugs

Run tests individually, predict what each should return, and compare that prediction with the result. Ask the interviewer to clarify ambiguous requirements—for example, the host confirms that a word should not count as containing itself.

Two bugs are found:

• The substring helper allowed a word to contain another word of equal length. The length condition should require the contained word to be strictly shorter.
• The word validator allowed an empty string. The tests indicate that empty strings and words containing spaces should be rejected.

Adding or strengthening a test can help confirm correctness, especially when an existing test checks only a weak condition.

3. Implement and optimize

Explain a solution before asking AI to write code. Review the generated code, run tests, and improve the solution if performance tests reveal a bottleneck.

The host estimates that candidates generally have about 50 working minutes in an hour-long interview.

Solutions Explored

1. Brute force

For each word, check every other word using the substring helper. Add a word to the results when a match is found, then stop checking it.

The host estimates the running time as O(N² × M), where N is the number of words and M is the average word length. This simple approach passes smaller tests but eventually fails a performance test on a large word list.

2. Generate substrings and check a set

Put all words in a set for fast membership checks. For each word, generate its possible substrings and check whether any is in the set. Exclude the word itself, since it is in the set and should not count as a container of itself.

This approach performs well on many tests, but Python string slicing creates new strings. The host initially estimates O(N × M²), then notes that creating all those substrings raises the practical bound to roughly O(N × M³). As a result, the approach slows down on tests with very long words.

3. Trie-based optimization

Build a trie containing the words. For each word, explore possible substrings by starting at each character and following matching trie edges. This avoids constructing a new sliced string for every candidate substring; instead, the algorithm follows characters through the trie.

The host concludes that the trie approach checks possible substrings without the same string-copying overhead, helping with long words. He briefly considers checking only suffixes, then realizes that would miss a contained word at other positions and revises the approach to check substrings more generally. The final tests pass, including the long-word performance test.

Lessons About Using AI in an Interview

• Use the AI rather than avoiding it. These interviews assess how candidates work with AI, not just whether they can solve the problem without it.
• Show your reasoning first. Explain the approach you are considering, then ask the AI to implement it or help investigate alternatives. A bare request like “implement the solver” gives the interviewer little insight into your judgment.
• Use specific, useful prompts. For example, request a concise codebase summary, an implementation of a stated approach, or independent optimization options.
• Avoid leading questions. Asking whether your own idea is correct can encourage the AI to agree with you. Ask for alternatives or ask the AI to challenge or disprove the approach.
• Review every change. Summarize what the generated code does, check that it matches the plan, and run relevant tests. Do not assume generated code is correct.
• Debug with evidence. When the set-based version fails, the host inspects intermediate values with a print statement and finds that each word was matching itself.
• Control context when needed. Clearing the chat or removing an earlier implementation can help prevent previous context from biasing the AI’s suggestions.
• Be careful with complexity analysis. Account for costs that are easy to overlook, such as copying substrings.
• Test incrementally. Running tests one at a time makes failures easier to understand than uncommenting and running many tests at once.

Speakers and Sources Featured

• Host/presenter: A former Meta staff engineer and Hello Interview co-founder, who plays the candidate, interviewer, and teacher during the walkthrough.
• AI coding assistant: Provides code summaries, implementation suggestions, optimization ideas, and complexity analysis; some suggestions require correction.
• Interview codebase and test suite: Sample files, instructions, and automated tests used as the exercise’s reference material.