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Apple·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Apple ML engineer interview with a coding problem around building a basic regex parser. Not the most grueling round but it required more thought than I expected going in.

Questions Asked (1)

Q1

Implement a basic regex parser that supports a defined subset of regex operations.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I went straight to recursion and kind of fumbled the base cases for a while.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the exact subset of regex operations (e.g., '.', '*', '?', character classes) and then outline a recursive or dynamic programming solution. Discuss trade-offs between different implementations (e.g., recursion vs. DP) and test with edge cases.

Pro tip: Demonstrate awareness of catastrophic backtracking and propose optimizations like memoization or iterative parsing to show depth. Also, relate the problem to ML applications, such as tokenization or pattern matching in data pipelines, to align with the role.

1. Clarify Requirements

Ask the interviewer to specify the exact regex features to support (e.g., '.', '*', '+', '?', character classes, anchors) and any constraints (e.g., input size, performance).

2. Choose Approach

Decide between recursive backtracking and dynamic programming. Explain the trade-offs: recursion is simpler but can be exponential; DP is more efficient but complex.

3. Design Algorithm

Outline the algorithm: parse the pattern into tokens, then match against the string using recursion or DP. Handle special characters like '*' (zero or more) and '.' (any character).

4. Implement and Test

Write clean code with helper functions. Test with edge cases: empty string, empty pattern, patterns with '*', '.', and combinations. Verify against expected outputs.

5. Analyze Complexity

Discuss time and space complexity. For DP, O(m*n) where m and n are lengths of pattern and string. For recursion, worst-case exponential. Mention optimizations like memoization.

Key Points to Mention

  • Handling of special characters: '.' matches any single character, '*' matches zero or more of the preceding element.
  • Recursive vs. dynamic programming approaches and their trade-offs in time/space complexity.
  • Edge cases: empty pattern, empty string, patterns like '.*', 'a*', and invalid patterns.
  • Optimization techniques: memoization to avoid redundant computations, iterative parsing to prevent stack overflow.
  • Real-world relevance: regex in ML for text preprocessing, tokenization, and feature extraction.
  • Testing strategy: unit tests with various inputs, including performance tests for large strings.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.