rippling·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Rippling software engineer interview, one round focused entirely on building an in-memory delivery cost and payment tracking system. The problem was more accounting-flavored than I expected for a coding screen, which threw me off a bit at first.

Questions Asked (1)

Design and implement an in-memory system for tracking delivery costs and driver payments, with APIs for adding drivers with hourly rates, recording deliveries with start and end times, getting the total cost, paying up to a given timestamp, and querying unpaid costs.

System DesignData ModelingAPI & Integrations

Author's notes

The hourly rate angle made this feel less like a typical coding question and more like a mini payroll engine.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and defining core entities (Driver, Delivery, Payment) and their relationships. Then design the data model and API contracts, focusing on how to compute costs and track payments efficiently. Finally, discuss implementation details, edge cases, and potential optimizations.

Pro tip: Emphasize the importance of idempotency and consistency in payment operations, and suggest using a ledger-based approach to track all financial transactions for auditability.

1. Clarify Requirements

Ask questions to understand constraints: expected scale, concurrency needs, precision of time and money, and whether payments can be partial or must be exact.

2. Define Data Model

Identify core entities: Driver (id, hourlyRate), Delivery (id, driverId, startTime, endTime, cost), Payment (id, driverId, timestamp, amount). Consider relationships and indexes.

3. Design APIs

Define method signatures for addDriver, recordDelivery, getTotalCost, payUpTo, and getUnpaidCost. Specify parameters, return types, and error handling.

4. Implement Core Logic

For each API, outline algorithms: cost calculation (duration * rate), payment processing (accumulate unpaid deliveries up to timestamp), and querying unpaid costs.

5. Address Edge Cases and Optimizations

Discuss handling overlapping deliveries, timezone issues, floating-point precision, concurrency, and potential optimizations like caching or event sourcing.

Key Points to Mention

Use of appropriate data structures (e.g., maps for drivers, lists for deliveries) and indexing for efficient queries.
Cost calculation: duration = endTime - startTime, cost = duration * hourlyRate; consider rounding and currency precision.
Payment semantics: payUpTo should pay all unpaid deliveries with endTime <= timestamp; handle partial payments if required.
Idempotency: ensure payUpTo can be called multiple times without double-paying.
Concurrency: use locks or transactional boundaries to prevent race conditions in payment processing.
Extensibility: design for future features like different rates, bonuses, or multi-currency support.

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