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Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Meta phone screen for a SWE role, centered entirely on palindrome problems. Classic Meta coding territory, and the follow-up extension to K deletions is where things get real.

Questions Asked (2)

Q1

Given a string, return whether it can be made into a palindrome by deleting at most one character.

Algorithms & Data Structures
Author's notes

Two-pointer skipping once on mismatch is the move.

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AI HintsAI Generated

Suggested Approach

Use a two-pointer technique starting from both ends of the string. When characters mismatch, check if skipping either the left or right character results in a palindrome. If either does, return true; otherwise, return false.

Pro tip: Clarify edge cases upfront, such as empty strings or strings with one character, and mention that the solution runs in O(n) time with O(1) space. This shows attention to detail and efficiency.

1. Clarify the problem

Confirm that deleting at most one character means zero or one deletion is allowed, and that the string can be empty or have any length. Ask if the string contains only lowercase letters or any characters.

2. Outline the two-pointer approach

Initialize two pointers at the start and end of the string. Move them inward while characters match. If a mismatch occurs, try skipping the left character or the right character and check if the remaining substring is a palindrome.

3. Implement the palindrome check helper

Write a helper function that checks if a substring (given by indices) is a palindrome using two pointers. This avoids code duplication.

4. Handle the mismatch case

When a mismatch is found, call the helper on the substring excluding the left character and on the substring excluding the right character. If either returns true, the answer is true.

5. Analyze complexity and edge cases

State that the time complexity is O(n) because each character is visited at most twice, and space complexity is O(1). Discuss edge cases like empty string, single character, and strings that are already palindromes.

Key Points to Mention

  • Two-pointer technique for palindrome checking
  • Greedy choice: when mismatch, try both skips
  • Helper function to check palindrome on substring
  • Time complexity O(n) and space complexity O(1)
  • Edge cases: empty string, single character, already palindrome
  • At most one deletion means zero or one deletion allowed

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

Q2

Follow-up: what if you can delete at most K characters instead of just one? How does your solution change?

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This is the part that separates people.

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AI HintsAI Generated

Suggested Approach

First, clarify the original problem and the current solution's complexity. Then, explain how allowing K deletions changes the problem, likely requiring a dynamic programming approach with a state dimension for deletions. Finally, discuss the time and space complexity trade-offs and potential optimizations.

Pro tip: Demonstrate awareness that K deletions often turns a simple two-pointer problem into a DP problem, but also mention that if K is small, you might still use a modified sliding window with a counter. This shows you consider constraints before jumping to a solution.

1. Clarify the problem and constraints

Restate the problem with K deletions and ask about constraints: is K fixed, what are the input sizes, and what is the expected complexity? This ensures you're solving the right problem.

2. Recall the original solution

Briefly describe the original solution for one deletion (e.g., two-pointer or DP) and its complexity. This sets a baseline for comparison.

3. Adapt to K deletions

Explain that with K deletions, a greedy two-pointer may not work; instead, use dynamic programming with states (i, j, k) where k is deletions used. Define the recurrence and base cases.

4. Analyze complexity and optimize

State the time and space complexity (e.g., O(n*m*K)) and discuss optimizations like reducing space to O(m*K) or using early termination if K is small.

5. Discuss trade-offs and edge cases

Mention trade-offs between DP and other approaches (e.g., BFS for edit distance), and handle edge cases like K >= length of string.

Key Points to Mention

  • Dynamic programming with state (i, j, k) where k is number of deletions used.
  • Time complexity O(n*m*K) and space complexity O(m*K) after optimization.
  • Comparison with the one-deletion case: two-pointer works for K=1 but not for general K.
  • Edge cases: K=0, K >= max deletions needed, empty strings.
  • Potential optimizations: early termination, using 1D arrays, or BFS if K is small.
  • Trade-offs: DP is more general but may be overkill if K is very small; sliding window with counter might work for specific problems.

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