rippling·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Rippling's AI coding round was a bit of a surprise format. You get to use an AI assistant during the problem, but the code still has to pass all the provided tests, so it's not a free pass by any means. Three sub-questions, all required, all around the same scenario.

Questions Asked (1)

Given a list of expense entries (each with fields like amount, category, date, employee, receipt, and approver) and a set of company policy rules (per-category caps, receipt requirements, approval thresholds, blackout categories, monthly totals per employee), implement a function that determines whether a submitted expense report is valid and returns any specific violations if not.

Algorithms & Data StructuresData ModelingTechnical Trade-offs

Author's notes

The scenario sounds manageable until you realize the policy rules interact in non-obvious ways.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then design a modular validation pipeline that separates policy checks from data traversal. Implement each rule as an independent validator that returns violations, and aggregate results to determine overall validity.

Pro tip: Emphasize extensibility: design the system so new policy rules can be added without modifying existing code, and discuss how you would handle rule conflicts or precedence.

1. Clarify Requirements and Edge Cases

Ask questions to understand ambiguous terms like 'blackout categories', 'approval thresholds', and how monthly totals are calculated (e.g., calendar month vs. rolling 30 days). Confirm expected output format for violations.

2. Design Data Model and Validation Architecture

Define data structures for expenses and policy rules. Propose a modular design where each rule is a separate function or class implementing a common interface, enabling easy addition of new rules.

3. Implement Core Validation Logic

Write functions to check each rule: per-category caps, receipt requirements, approval thresholds, blackout categories, and monthly totals per employee. Ensure efficient traversal, possibly grouping expenses by employee and month.

4. Aggregate Violations and Return Result

Collect all violations from individual checks into a list, and determine overall validity (valid if no violations). Return a structured response with a boolean and the list of violations.

5. Discuss Trade-offs and Extensibility

Talk about performance considerations (e.g., O(n) vs. O(n log n) for grouping), and how the design supports adding new rules or changing policy without major refactoring.

Key Points to Mention

Modular design with independent validators for each policy rule
Efficient grouping of expenses by employee and month for monthly total checks
Handling edge cases like missing receipts, multiple violations, and rule precedence
Clear separation of concerns between data parsing, rule evaluation, and result aggregation
Extensibility to accommodate new rules or changes in policy
Time and space complexity analysis of the solution

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