Stripe·Software Engineer·Technical Phone Screen·Senior

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Jul 2026Remote

Summary

Stripe frontend coding round, one big component question that looked manageable but had enough wrinkles to keep you honest for the full session.

Questions Asked (1)

Build a UI component that accepts an array of name/amount pairs, renders the list with a text search filter and a minimum-amount filter, and highlights the item(s) with the highest amount in the currently filtered results in real time. Also explain your state management approach, how you compute the max efficiently, the time/space complexity, and describe edge-case test scenarios including empty lists, ties, non-ASCII names, and rapid typing.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then walk through your component design and state management choices. Explain how you compute the max efficiently in a single pass, analyze time/space complexity, and discuss edge cases and testing strategies. Emphasize real-time updates and performance considerations for rapid typing.

Pro tip: Demonstrate awareness of performance trade-offs: debouncing input for rapid typing, using memoization to avoid redundant computations, and ensuring the max computation is O(n) per filter change. Also, mention accessibility and internationalization for non-ASCII names.

1. Clarify Requirements and Constraints

Ask about expected data size, update frequency, and whether filtering should be case-insensitive or locale-aware. Confirm that the max highlight updates in real time as filters change.

2. Design Component and State Management

Outline the component structure: a list, two filter inputs, and highlighted items. Choose state management (e.g., React useState/useReducer or equivalent) to hold the original array, filter values, and derived filtered results.

3. Implement Filtering and Max Computation

Describe filtering logic: apply text search (e.g., includes, case-insensitive) and minimum amount filter. Compute the max amount in the filtered results in a single pass, then highlight items matching that max.

4. Analyze Complexity and Optimize

State time complexity: O(n) per filter change for filtering and max computation, where n is the number of items. Space complexity: O(n) for filtered results. Discuss optimizations like debouncing for rapid typing and memoization.

5. Discuss Edge Cases and Testing

Cover edge cases: empty list (no highlight), ties (highlight all max items), non-ASCII names (use locale-aware comparison), rapid typing (debounce to avoid excessive re-renders). Propose unit tests for each scenario.

Key Points to Mention

State management: keep source data immutable, derive filtered results and max on each render or via memoization.
Efficient max computation: single pass through filtered array, O(n) time, no sorting needed.
Time/space complexity: O(n) time per filter change, O(n) space for filtered results; overall O(n) per update.
Edge cases: empty list, ties (multiple max items), non-ASCII names (use localeCompare or normalize), rapid typing (debounce input).
Real-time updates: use controlled inputs and ensure re-computation triggers on filter changes.
Testing: unit tests for filtering, max highlighting, and edge cases; consider integration tests for rapid typing.

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