Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Stripe coding screen for a software engineer role. The problem was about picking factories from a jagged 2D array to minimize total production cost. Pretty clean problem once you see the trick.

Questions Asked (1)

You're given a jagged 2D array where each row is a factory type and each element holds a distance and a production cost. You must pick exactly one factory from each row. For this part, ignore distance and find the minimum total production cost.

Algorithms & Data Structures

Author's notes

The distance field is a red herring for part 1 and I almost overthought it.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify that the problem reduces to selecting the minimum cost from each row independently, since distance is ignored and there are no cross-row constraints. Then, for each row, find the minimum production cost and sum them up. Discuss time and space complexity, and mention edge cases like empty rows or negative costs.

Pro tip: Mention that this is a greedy approach that works because the choices are independent; if there were constraints like a budget or dependencies, dynamic programming would be needed. Also, note that the input is jagged, so handle rows of varying lengths.

1. Understand the problem

Restate the problem: pick exactly one factory from each row, ignore distance, minimize total production cost. Confirm that there are no constraints linking choices across rows.

2. Identify the optimal substructure

Recognize that the total minimum cost is the sum of the minimum cost in each row, because the rows are independent. This is a greedy choice.

3. Design the algorithm

Iterate through each row, find the minimum production cost in that row, and add it to a running total. Return the total.

4. Analyze complexity

Time complexity is O(N) where N is the total number of elements, as each element is visited once. Space complexity is O(1) extra space.

5. Handle edge cases

Consider empty input, rows with no elements, or negative costs. If any row is empty, the problem is invalid; otherwise, negative costs are fine and the algorithm still works.

Key Points to Mention

Independence of rows: no constraints across rows, so greedy works.
Time complexity: O(N) where N is total number of elements.
Space complexity: O(1) extra space.
Edge cases: empty rows, negative costs, single row.
Comparison to dynamic programming: if constraints existed, DP would be needed.
Jagged array handling: rows may have different lengths.

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