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Pinterest·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Pinterest phone screen for a software engineering role, one coding problem the whole time. Pretty focused on data structures and complexity analysis, not just getting the code to work.

Questions Asked (1)

Q1

You're given a call log of phone numbers and a list of user-reported spam numbers (with possible duplicates). Cross-reference the two to output each spam number that appears in the call log along with how many times it was reported. Walk through your algorithm, choice of data structures, how you'd handle invalid numbers, and the time and space complexity. Then write the code.

Algorithms & Data StructuresTechnical Trade-offsSystem Design
Author's notes

The core idea clicked fast, use a hash map to count reports, then check each call log entry against it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then propose a hash map-based solution that counts spam reports and checks against the call log. Walk through the algorithm step-by-step, justify data structure choices, and analyze time/space complexity before writing clean code.

Pro tip: Mention that you'd deduplicate spam reports using a set or counter, and discuss how to handle invalid numbers (e.g., non-numeric, wrong length) by filtering them out early to avoid skewing results.

1. Clarify requirements and edge cases

Ask about input formats, definition of invalid numbers, and expected output order. Confirm whether duplicates in spam list should be counted multiple times.

2. Design the algorithm and data structures

Propose using a hash map to count spam reports (key: number, value: count) and a set for the call log for O(1) lookups. Explain why this is efficient.

3. Walk through the steps and handle invalid numbers

Describe iterating through the spam list to build the count map, filtering invalid numbers. Then iterate through the call log, checking against the map and collecting results.

4. Analyze time and space complexity

State that time complexity is O(n + m) where n is call log size and m is spam list size, and space complexity is O(k) where k is unique spam numbers.

5. Write the code

Implement the solution in a clean, modular way, with comments and handling of edge cases. Test with a small example.

Key Points to Mention

  • Use a hash map to count spam reports efficiently, handling duplicates by incrementing counts.
  • Use a set for the call log to enable O(1) membership checks.
  • Filter invalid numbers early (e.g., using regex or validation) to avoid errors and ensure data quality.
  • Time complexity: O(n + m) for processing both lists; space complexity: O(k) for storing unique spam numbers and their counts.
  • Consider output format: return a list of tuples or a map of spam number to report count.
  • Discuss potential optimizations or trade-offs, such as using a Bloom filter for very large call logs if memory is constrained.

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