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Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026Remote

Summary

Meta phone screen for a software engineer role, focused entirely on string parsing. The main problem was straightforward enough but the follow-ups got tricky fast and I don't think I handled the complexity analysis part particularly well.

Questions Asked (3)

Q1

Implement a function that checks whether a given abbreviation is valid for a given word, where a number in the abbreviation means that many characters are skipped and leading zeros are not allowed.

Algorithms & Data Structures
Author's notes

Pretty clean to code once you think through the edge cases.

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AI HintsAI Generated

Suggested Approach

Use a two-pointer technique to traverse the word and abbreviation simultaneously. When encountering a digit in the abbreviation, parse the full number (ensuring no leading zeros) and skip that many characters in the word. At the end, both pointers should have reached the end of their respective strings.

Pro tip: Clarify edge cases upfront: empty strings, abbreviation with only numbers, and numbers with leading zeros. Also, consider using a single pass with careful index management to avoid off-by-one errors.

1. Clarify requirements and edge cases

Confirm that leading zeros are invalid, numbers represent skips, and both strings may be empty. Discuss examples like 'a' vs '1' (valid) and 'a' vs '01' (invalid).

2. Initialize two pointers

Set pointers i for word and j for abbreviation, both starting at 0. Plan to iterate until both reach the end.

3. Traverse and compare

While j < len(abbr): if abbr[j] is a digit, parse the number (check for leading zero), advance i by that number; else, compare characters and advance both pointers.

4. Validate final positions

After the loop, ensure both i and j have reached the end of their strings. If not, return false.

5. Test with edge cases

Run through cases like empty strings, all-digit abbreviations, and mismatched lengths to verify correctness.

Key Points to Mention

  • Two-pointer technique for simultaneous traversal
  • Handling multi-digit numbers and leading zeros
  • Edge cases: empty strings, abbreviation longer than word, numbers exceeding word length
  • Time complexity O(n) and space complexity O(1)
  • Character comparison when abbreviation has letters
  • Ensuring both pointers reach the end

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

Q2

Given two abbreviations, determine whether there is at least one word that both could validly represent. If yes, return true and show one possible alignment of which positions are kept vs skipped.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This is where things got uncomfortable.

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AI HintsAI Generated

Suggested Approach

Model the problem as a two-pointer scan over the abbreviations, where each pointer can either match the current character or skip it. Use recursion with memoization to avoid exponential recomputation, and reconstruct the alignment by storing decisions. Return true as soon as a valid alignment is found, and backtrack to output one alignment.

Pro tip: Clarify whether the abbreviations are case-sensitive and whether they must represent the same word or can represent different words. Also, mention that the problem is equivalent to finding a common subsequence of the two abbreviations, which can be solved in O(n*m) time with DP.

1. Clarify the problem

Ask if the abbreviations are case-sensitive, if they must represent the same word, and if the word must be a valid English word or any string. Confirm that we need to return one alignment if it exists.

2. Define the state

Let i and j be indices into the two abbreviations. At each step, we can either match the current characters (if they are equal) and advance both, or skip a character in one abbreviation and advance only that pointer.

3. Use DP with memoization

Create a memo table to store whether a solution exists from state (i, j). Use recursion to explore match and skip options, caching results to avoid recomputation. Time complexity O(n*m).

4. Reconstruct the alignment

During recursion, store the choice made at each state (match, skip first, skip second). Once a solution is found, backtrack from (0,0) to build the alignment of kept vs skipped positions.

5. Return result

If a solution exists, return true and the alignment; otherwise return false. Discuss edge cases like empty strings and no common characters.

Key Points to Mention

  • Two-pointer technique with branching (match or skip)
  • Dynamic programming with memoization to avoid exponential time
  • Time and space complexity: O(n*m) time, O(n*m) space (can be optimized to O(min(n,m)) space)
  • Reconstruction of the alignment using parent pointers or stored decisions
  • Edge cases: empty abbreviations, no common characters, identical abbreviations
  • Relation to longest common subsequence (LCS) problem

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

Q3

What is the time and space complexity of your solution, and how would you adapt it for very long inputs or a streaming validation scenario?

Technical Trade-offsSystem Design
Author's notes

Blanked a little here.

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AI HintsAI Generated

Suggested Approach

Start by clearly stating the time and space complexity of your solution using Big-O notation, then explain how you would adapt it for long inputs or streaming by discussing trade-offs between memory and time, and proposing techniques like chunking, sliding windows, or external sorting. Emphasize that the adaptation depends on the specific constraints and validation requirements.

Pro tip: Demonstrate awareness of real-world constraints by mentioning that streaming validation often requires a shift from batch processing to incremental algorithms, and that you would consider probabilistic data structures like Bloom filters when exact answers are not feasible.

1. State the baseline complexity

Clearly articulate the time and space complexity of your original solution, specifying the variables (e.g., n = input size) and whether it's average or worst case.

2. Identify bottlenecks for long inputs

Discuss which parts of your solution scale poorly with input size, such as memory usage for storing the entire input or time complexity that grows superlinearly.

3. Propose streaming adaptations

Suggest modifications like processing input in chunks, using a sliding window, or maintaining only necessary state to achieve O(1) or O(k) space where k is small.

4. Discuss trade-offs and validation guarantees

Explain how streaming might affect correctness (e.g., approximate results) and how you would handle validation, such as early termination or incremental checks.

5. Summarize with a recommendation

Conclude by recommending a specific approach based on typical constraints, and mention any additional considerations like parallelism or external storage.

Key Points to Mention

  • Big-O notation for time and space, with clear definitions of variables
  • Trade-offs between time and space when adapting for streaming
  • Techniques like chunking, sliding window, or online algorithms
  • Use of probabilistic data structures (e.g., Bloom filter, Count-Min Sketch) for approximate validation
  • Handling of edge cases like infinite streams or memory limits
  • Impact on validation correctness and how to maintain guarantees

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