rippling·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Rippling SWE interview with a meaty system design problem around a corporate card rules engine. The core ask was extending a simple policy evaluator to support composite boolean logic, then talking through and partially implementing optimizations. Pretty involved for a single session.

Questions Asked (1)

You have a rules engine that checks per-expense policies against incoming expenses. Extend it to support composite rules using AND, OR, and NOT operators, so you can express things like '(vendor is restaurant AND amount > 50)' or '(amount > 100) AND NOT (vendor is Staples)'. Then explain and implement optimizations for evaluating these composite rules efficiently.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This was a lot to hold in your head at once.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and designing a composite pattern with a common interface for leaf and composite rules. Then discuss evaluation strategies, focusing on short-circuiting and optimizations like indexing and caching. Finally, implement a clean, extensible solution with tests.

Pro tip: Emphasize that short-circuit evaluation is not just an optimization but also a correctness feature for rules with side effects or expensive checks. Also, mention that the composite pattern enables easy addition of new operators without modifying existing code.

1. Clarify Requirements and Constraints

Ask about rule complexity, performance needs, and whether rules can be dynamically updated. Confirm that the rules engine should support AND, OR, NOT and that expenses are evaluated individually.

2. Design the Rule Interface and Composite Structure

Define a common Rule interface with an evaluate(expense) method. Implement leaf rules (e.g., VendorIs, AmountGreaterThan) and composite rules (AndRule, OrRule, NotRule) that hold child rules.

3. Implement Evaluation with Short-Circuiting

For AND, evaluate children sequentially and return false on first false; for OR, return true on first true; for NOT, invert the child's result. This avoids unnecessary evaluations.

4. Optimize for Performance

Discuss optimizations: reorder children by cost or selectivity, cache results for repeated sub-rules, compile rules into a decision tree or bytecode, and use indexing for leaf conditions.

5. Test and Validate

Write unit tests for each rule type and composite combinations, including edge cases like empty composites. Benchmark to ensure optimizations yield improvements.

Key Points to Mention

Composite pattern for uniform treatment of leaf and composite rules
Short-circuit evaluation for AND/OR to improve performance and avoid side effects
Rule ordering based on cost and selectivity to minimize evaluations
Caching or memoization of rule results for repeated evaluations
Compilation of rules into an optimized form (e.g., decision tree, bytecode)
Extensibility: adding new operators or leaf rules without modifying existing code

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