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Stripe·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026Remote

Summary

Stripe coding screen, probably for a backend or data engineering role. The problem was a record linkage thing where you score pairs of user records using weighted field similarity and return which ones are linked to a target. Sounded manageable until I actually had to think through the similarity function design.

Questions Asked (1)

Q1

You're given a list of user records, each with an id, name, email, and company field. Each field has a weight (summing to 1). Two records are considered the same user if their weighted similarity score meets or exceeds a threshold. Write a function that returns all record ids directly linked to a given target record.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

The setup took me a minute to internalize.

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AI HintsAI Generated

Suggested Approach

Clarify the input format and similarity metric, then propose an efficient algorithm that avoids comparing all pairs by using blocking or indexing on high-weight fields. Implement the function with a focus on correctness and complexity, and discuss trade-offs for scaling to large datasets.

Pro tip: Mention that in production systems like Stripe, you'd likely use a blocking technique (e.g., exact match on email or company) to reduce the candidate set before computing weighted similarity, and discuss how to handle missing fields or weight adjustments.

1. Clarify requirements and assumptions

Ask about the similarity metric (e.g., exact match, Jaccard, edit distance), field weights, threshold value, and whether records are static or dynamic. Confirm the output should be direct links only (not transitive).

2. Design the similarity function

Define how to compute weighted similarity: for each field, compute a similarity score (e.g., 1 if equal, 0 otherwise, or a string similarity), multiply by its weight, and sum. Ensure the weights sum to 1.

3. Choose an efficient algorithm

Avoid O(n^2) by using blocking: group records by exact match on high-weight fields (e.g., email or company) to reduce comparisons. For each candidate, compute similarity and check against threshold.

4. Implement and test

Write the function, handling edge cases like missing fields, empty lists, and threshold boundaries. Test with small examples and consider performance for large datasets.

5. Discuss trade-offs and scalability

Talk about time/space complexity, potential for false positives/negatives, and how to scale (e.g., distributed processing, indexing). Mention alternative approaches like locality-sensitive hashing.

Key Points to Mention

  • Weighted similarity computation: sum of field similarities multiplied by weights, compared to threshold.
  • Blocking/indexing to reduce candidate pairs, e.g., exact match on email or company.
  • Handling missing or null fields and their impact on similarity.
  • Time and space complexity: naive O(n^2) vs. optimized with blocking.
  • Direct links only: ensure no transitive closure unless specified.
  • Scalability considerations: distributed systems, approximate algorithms, and trade-offs.

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