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Pinterest·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Apr 2026Remote

Summary

Pinterest MLE interview with a classic group expense balancing problem. Pretty much a direct lift from LC 465 so if you've seen it before you're in decent shape, but the backtracking angle can trip you up if you're not warmed up on DFS.

Questions Asked (1)

Q1

Given a group trip where each person paid different amounts for shared expenses, find the minimum number of transactions needed to settle all debts among the group.

Algorithms & Data Structures
Author's notes

Knew this one from grinding leetcode but still fumbled the opening explanation.

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AI HintsAI Generated

Suggested Approach

Model the problem as a graph where each person is a node and debts are edges, then compute each person's net balance. The goal is to minimize transactions by greedily matching the largest creditor with the largest debtor, which is a common heuristic for the debt settlement problem.

Pro tip: Acknowledge that the greedy approach is not always optimal (the problem is NP-hard), but it works well in practice and is what most interviewers expect. Mention that for small groups, you could use backtracking or dynamic programming to find the exact minimum.

1. Clarify the problem

Ask if the goal is to minimize the number of transactions or the total amount transferred, and confirm that all debts are to be settled exactly.

2. Compute net balances

For each person, calculate the net amount they owe or are owed by summing all their transactions. This reduces the problem to settling these net balances.

3. Separate creditors and debtors

Create two lists: one for people with positive net balance (creditors) and one for negative net balance (debtors).

4. Greedy matching

Repeatedly match the largest creditor with the largest debtor, settling the smaller of the two amounts, and update their balances. Count each match as one transaction.

5. Analyze complexity and optimality

Discuss the time complexity (O(n log n) with sorting) and note that while greedy is not always optimal, it often yields the minimum or near-minimum number of transactions.

Key Points to Mention

  • Net balance calculation to simplify the problem
  • Graph representation: nodes as people, edges as debts
  • Greedy algorithm: match largest creditor with largest debtor
  • Time complexity: O(n log n) due to sorting
  • NP-hardness of the exact minimum transaction problem
  • Alternative approaches: backtracking, dynamic programming for small n

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