Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Stripe coding round with a graph/similarity problem that looked like a straightforward matching exercise until the indirect-hop requirement showed up. Not a typical LeetCode grind, more of a domain-modeling problem with some BFS flavor to it.

Questions Asked (1)

Given a list of user records where each field has an associated weight, a target user ID, and a similarity threshold, find all users similar to the target either directly (match score exceeds threshold) or indirectly through exactly one intermediate user where each consecutive pair is directly similar. Return the combined set.

Algorithms & Data StructuresSystem Design

Author's notes

The weighted field matching part was fine, just iterate and sum.

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AI HintsAI Generated

Suggested Approach

Model the problem as a graph where users are nodes and edges exist between users whose weighted similarity score exceeds the threshold. Then perform a BFS from the target user up to depth 2 to collect all directly and indirectly similar users, ensuring no duplicates. Discuss the algorithm's complexity and potential optimizations for large-scale data.

Pro tip: Mention that in a real system like Stripe, you'd likely precompute similarities or use approximate nearest neighbor techniques to avoid O(n^2) comparisons, and clarify whether the threshold applies to raw scores or normalized scores.

1. Clarify requirements and assumptions

Confirm the similarity metric (e.g., weighted sum, cosine similarity), how weights are applied, and whether the threshold is inclusive. Ask about data size, expected number of similar users, and if the graph is directed or undirected.

2. Design the similarity computation

Define a function to compute the weighted similarity score between two users. Discuss normalization if needed and how to efficiently compute scores for all pairs or only relevant pairs.

3. Build the similarity graph

Create an adjacency list where an edge exists between users if their similarity score exceeds the threshold. Consider memory and time trade-offs for building the graph.

4. Traverse to find similar users

Perform a BFS from the target user up to depth 2, collecting all visited users. Use a set to avoid duplicates and ensure the target itself is excluded from the result.

5. Analyze complexity and optimize

Discuss time and space complexity (e.g., O(n^2) for pairwise comparisons). Suggest optimizations like indexing, pruning, or approximate methods for large datasets.

Key Points to Mention

Graph representation: users as nodes, similarities as edges.
BFS traversal with depth limit 2 to capture direct and indirect connections.
Handling duplicates and excluding the target user from the result.
Time and space complexity: O(n^2) for similarity computation, O(n + e) for traversal.
Optimization strategies: precomputation, approximate nearest neighbors, or distributed processing.
Edge cases: no similar users, threshold at boundary, large user base.

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