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rippling·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026Remote

Summary

Rippling SWE interview with a tree/graph problem that sounds deceptively clean but has some real nuance once you get into the greedy logic. One coding round, algorithmic focus.

Questions Asked (1)

Q1

Given a company org tree rooted at the CEO, find the minimum number of employees you need to 'promote' (i.e. reassign to report directly to the CEO) so that no employee's path to the CEO exceeds a given length h.

Algorithms & Data Structures
Author's notes

The setup clicked pretty fast but the greedy part took me a minute.

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AI HintsAI Generated

Suggested Approach

Model the problem as a tree where each node has a depth from the CEO. Use a greedy bottom-up approach: for each node, compute the maximum depth in its subtree; if it exceeds h, promote the node itself (or an ancestor) to cut the depth. The goal is to minimize promotions while ensuring all leaves are within depth h.

Pro tip: Clarify whether promotions can be applied to any employee or only to managers, and whether the tree structure changes after promotion. Also, discuss the trade-off between greedy and dynamic programming approaches, showing awareness of optimality.

1. Clarify the problem

Ask about constraints: tree size, h value, whether promotions can be repeated, and if the CEO's direct reports count as depth 1. Confirm that promoting an employee means they and their subtree move up.

2. Define depth and promotion effect

Define depth as number of edges from CEO. Promoting a node reduces the depth of its entire subtree by the difference between its current depth and 1 (or 0 if CEO is depth 0).

3. Greedy bottom-up strategy

Traverse the tree post-order. For each node, compute the maximum depth of any leaf in its subtree. If that depth exceeds h, promote the node (or an ancestor) to cut the subtree. Count promotions.

4. Prove optimality

Argue that promoting the highest possible node that still keeps the subtree within h is optimal, as it covers the most nodes with one promotion. Use exchange argument.

5. Analyze complexity

The algorithm runs in O(n) time and O(n) space for a tree with n nodes, using a single DFS. Mention that if h is small, we might need multiple promotions per path.

Key Points to Mention

  • Tree traversal (DFS/BFS) and depth calculation
  • Greedy algorithm vs dynamic programming
  • Optimality proof using exchange argument
  • Time and space complexity analysis
  • Handling edge cases: h=0, h=1, skewed trees
  • Clarifying assumptions about promotion mechanics

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