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rippling·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026Remote

Summary

Rippling software engineer screening with two back-to-back coding problems. Nothing too exotic but the follow-ups on the first one had some teeth to them.

Questions Asked (2)

Q1

Design an in-memory balance tracker for drivers on a delivery platform. It needs to support applying a delta to a driver's balance, fetching one driver's balance, and fetching the total balance across all drivers. Follow-up: how do you keep get_total_balance efficient when it's called very frequently? Second follow-up: how would you answer queries about the cumulative total after the first k updates in a sequence?

Algorithms & Data StructuresTechnical Trade-offsSystem Design
Author's notes

The naive version took me like two minutes, just a hashmap and a running total variable.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a simple hash map for per-driver balances and a running total for O(1) updates and total queries. For the follow-ups, discuss trade-offs: caching the total with incremental updates, and for cumulative totals after k updates, consider prefix sums or a Fenwick tree (BIT) to answer queries efficiently.

Pro tip: Mention that you'd keep the running total consistent with updates by updating it atomically in the same operation, and for the cumulative query follow-up, note that if updates are known in advance, you can precompute prefix sums; otherwise, a Fenwick tree gives O(log n) per query and update.

1. Clarify requirements and constraints

Ask about expected number of drivers, update frequency, query patterns, and whether balances can be negative. Confirm if operations need to be thread-safe.

2. Design core data structures

Use a hash map (driver_id -> balance) for per-driver balances and maintain a running total. Explain that apply_delta updates both the map and total in O(1), get_balance is O(1), and get_total_balance is O(1).

3. Address frequent get_total_balance

Since total is maintained incrementally, get_total_balance is already O(1). Discuss trade-offs: if updates are batched, you might recompute total periodically, but incremental is best for frequent queries.

4. Handle cumulative total after k updates

For queries like 'total after first k updates', if updates are known in advance, precompute prefix sums of deltas. For dynamic updates, use a Fenwick tree (BIT) over the sequence of updates to get prefix sums in O(log n).

5. Discuss trade-offs and scalability

Compare approaches: simple running total vs. Fenwick tree for cumulative queries. Mention memory vs. speed, and how to handle concurrency (e.g., locks or atomic operations).

Key Points to Mention

  • Hash map for per-driver balances and a running total for O(1) total queries.
  • Incremental update of total on each apply_delta to keep get_total_balance efficient.
  • Fenwick tree (Binary Indexed Tree) for cumulative totals after k updates with O(log n) per query/update.
  • Precomputing prefix sums if the sequence of updates is known in advance.
  • Thread-safety considerations: locks, atomic operations, or concurrent data structures.
  • Trade-offs between memory usage and query speed, and handling negative balances.

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

Q2

Given a list of closed intervals, merge all overlapping ones and return the result sorted by start time.

Algorithms & Data Structures
Author's notes

Classic.

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AI HintsAI Generated

Suggested Approach

Start by clarifying that the intervals are closed and the input list may be unsorted. Then propose sorting by start time and using a linear scan to merge overlapping intervals, explaining the logic and edge cases. Finally, analyze time and space complexity.

Pro tip: Mention that sorting is necessary for an efficient O(n log n) solution, and that in-place merging can save space if the input can be modified. Also, discuss how to handle edge cases like empty input or intervals that touch at endpoints.

1. Clarify assumptions and edge cases

Confirm that intervals are closed (e.g., [1,3] and [3,5] overlap), input may be unsorted, and handle empty or single-interval lists.

2. Sort intervals by start time

Sort the list of intervals based on their start times to bring potential overlaps together, enabling a linear scan.

3. Iterate and merge overlapping intervals

Initialize a result list with the first interval. For each subsequent interval, if it overlaps with the last interval in the result, merge them by updating the end time; otherwise, add it to the result.

4. Analyze complexity and discuss optimizations

State that time complexity is O(n log n) due to sorting, and space complexity is O(n) for the output (or O(1) extra if merging in-place). Mention that in-place merging is possible if the input can be modified.

Key Points to Mention

  • Sorting by start time is crucial for an efficient O(n log n) solution.
  • Overlap condition: next.start <= current.end (for closed intervals).
  • Merging by updating the end to max(current.end, next.end).
  • Edge cases: empty input, single interval, intervals that touch at endpoints.
  • Time complexity: O(n log n) due to sorting; space complexity: O(n) for output.
  • In-place merging can achieve O(1) extra space if input modification is allowed.

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