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Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Meta software engineer coding round, two problems back to back with a hard requirement to walk through time and space complexity for each. Pretty standard format but the complexity analysis part felt like it carried real weight.

Questions Asked (1)

Q1

Solve a coding problem and clearly explain the time and space complexity of your solution.

Algorithms & Data Structures
Author's notes

They were serious about the complexity breakdown, not just a quick mention at the end.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then discuss a brute-force solution and optimize it using appropriate data structures and algorithms. Implement the optimized solution with clean code, test with edge cases, and clearly explain the time and space complexity of each step.

Pro tip: Always state the complexity of your initial brute-force approach before optimizing, and after implementing the optimized solution, explicitly compare the complexities to show the improvement. This demonstrates structured thinking and cost-benefit analysis.

1. Understand and Clarify

Restate the problem in your own words, ask clarifying questions about input size, constraints, and edge cases, and confirm expected output format.

2. Brainstorm and Compare Approaches

Propose a brute-force solution first, then discuss potential optimizations using data structures or algorithmic techniques, comparing their time and space complexities.

3. Implement the Optimal Solution

Write clean, modular code for the chosen approach, explaining your reasoning as you go and handling edge cases.

4. Test and Validate

Walk through the code with a few test cases, including edge cases, to verify correctness and catch any bugs.

5. Analyze Complexity

Clearly state the time and space complexity of your final solution, breaking down the cost of each part and justifying with Big-O notation.

Key Points to Mention

  • Time complexity analysis with Big-O notation, including best, average, and worst cases.
  • Space complexity, distinguishing between auxiliary space and input space.
  • Trade-offs between different approaches (e.g., time vs. space).
  • Edge cases and how they affect complexity.
  • Use of appropriate data structures and their impact on performance.
  • Clear communication of assumptions and constraints.

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