Stripe·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Stripe coding round for a software engineering role. One meaty optimization problem that looked like a graph traversal at first but turned out to be a DP problem once I sat with it for a minute.

Questions Asked (1)

You have multiple groups of factories, each group representing a different type. Every factory has a distance from origin and a production cost. Pick exactly one factory per type. The total cost is the sum of production costs plus pairwise transport costs between consecutive types (transport cost depends on the distances of the two chosen factories). Find the minimum total cost.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was to try something greedy, which was wrong.

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AI HintsAI Generated

Suggested Approach

Model the problem as a dynamic programming over types, where the state is the chosen factory from the previous type. For each type, compute the minimum cost to reach each factory by considering all factories from the previous type, adding production and transport costs. Optimize using the structure of the transport cost (likely absolute distance difference) to reduce time complexity.

Pro tip: Clarify the transport cost function early—if it's proportional to |d_i - d_j|, you can optimize the DP using prefix/suffix minima, turning an O(N^2) solution into O(N). Always discuss trade-offs between simplicity and efficiency.

1. Clarify problem constraints and cost function

Ask about the number of types, factories per type, and the exact transport cost formula (e.g., |d_i - d_j| or squared difference). Confirm if distances are integers and if costs can be negative.

2. Define DP state and recurrence

Let dp[i][j] be the minimum total cost to choose a factory j from type i, considering all previous types. Recurrence: dp[i][j] = production_cost[i][j] + min_{k in type i-1} (dp[i-1][k] + transport_cost(d[i-1][k], d[i][j])).

3. Analyze naive complexity and optimize

Naive DP is O(M * N^2) where M is number of types and N is factories per type. If transport cost is |d_i - d_j|, optimize by sorting factories by distance and using prefix/suffix minima to compute min over k in O(N) per type, achieving O(M * N).

4. Handle edge cases and implement

Consider single type (no transport cost), large distances (use 64-bit integers), and ties. Implement the optimized DP with careful indexing and test with small examples.

5. Discuss trade-offs and extensions

Mention that if transport cost is arbitrary, O(M * N^2) might be necessary. For Stripe, emphasize scalability and potential to use convex hull trick if cost is quadratic.

Key Points to Mention

Dynamic programming with state representing the last chosen factory.
Transport cost function and its properties (e.g., absolute difference, metric).
Optimization using prefix/suffix minima for absolute difference cost.
Time and space complexity analysis (naive vs optimized).
Edge cases: single type, large inputs, integer overflow.
Trade-offs between different optimization techniques (e.g., convex hull trick for quadratic costs).

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