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Palantir·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jul 2026

Summary

Palantir coding round, one meaty problem about employees and shared interests. The question had two parts and they wanted complexity analysis on top of working code, which I wasn't fully expecting.

Questions Asked (1)

Q1

Given N employees, each with an integer id and a set of interests (strings), write an algorithm to find all employees who share at least one common interest. You can return either a mapping from each interest to the sorted list of employee ids who have it, or the list of all unique employee-id pairs that share one or more interests (or both). Analyze time and space complexity.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The first part, building the interest-to-employees map, felt manageable.

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AI HintsAI Generated

Suggested Approach

Clarify the output format and constraints, then propose an inverted index mapping each interest to a sorted list of employee IDs. For pair generation, iterate over each interest's employee list and collect unique pairs in a set, then analyze time and space complexity for both approaches.

Pro tip: Mention that the inverted index is the standard approach for this type of problem and that pair generation can be optimized by only considering employees within the same interest group, avoiding O(N^2) comparisons.

1. Clarify requirements and constraints

Ask about input size, whether employee IDs are unique, if interests are case-sensitive, and which output format is preferred. Confirm if duplicate pairs should be removed.

2. Design the inverted index

Create a hash map from each interest to a list of employee IDs. Iterate through all employees and their interests, appending the employee ID to the corresponding list.

3. Generate pairs from the index

For each interest's employee list, generate all unique pairs (i, j) with i < j and insert them into a set to avoid duplicates. Alternatively, return the inverted index directly if that's the desired output.

4. Analyze complexity

Time: O(N * A + P) where A is average interests per employee and P is total pairs generated. Space: O(N * A) for the index and O(P) for the pair set. Discuss trade-offs between the two outputs.

5. Optimize and discuss edge cases

Consider sorting employee lists for consistent output, handling empty interests, and using efficient data structures. Mention that pair generation can be expensive if interests are very common.

Key Points to Mention

  • Inverted index (hash map from interest to employee IDs) is the core data structure.
  • Time complexity: O(N * A + P) where P is the number of unique pairs; space complexity: O(N * A + P).
  • Use a set to deduplicate pairs when generating all unique employee pairs.
  • Sorting employee IDs within each interest list ensures deterministic output.
  • Trade-off: returning the inverted index is more space-efficient than enumerating all pairs when interests are dense.
  • Edge cases: employees with no interests, duplicate interests per employee, and case sensitivity.

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