Ramp·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026Remote

Summary

Ramp technical phone screen for a software engineer role. One meaty design-and-implement question that took up the whole session, basically a mini system design disguised as a coding problem.

Questions Asked (1)

Design and implement an in-memory storage engine for a multi-level warehouse where items have weights and expiration times. Support a store operation that places items on the topmost available level, and a retrieve operation that finds the heaviest item from the topmost level that is at least half full after expiring stale entries.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This looked like a coding question but it was really a system design problem crammed into a class implementation.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a data structure that efficiently supports both operations. For the store operation, maintain levels in a stack-like structure with capacity tracking; for retrieve, use a max-heap per level to find the heaviest item, and lazily remove expired items. Discuss trade-offs between different data structures and consider edge cases.

Pro tip: Demonstrate awareness of real-world constraints by discussing how to handle expiration efficiently—e.g., using lazy deletion with periodic cleanup to avoid O(n) scans on every operation. Also, mention that you would confirm the definition of 'at least half full' (by count or weight) and whether levels are fixed capacity.

1. Clarify Requirements and Assumptions

Ask questions to understand the exact behavior: What is the capacity of each level? Is 'half full' based on item count or total weight? How are expiration times handled? Are there concurrency requirements? This ensures you design the right solution.

2. Design Data Structures

Propose a list of levels, each with a capacity and a max-heap (or balanced BST) keyed by weight for efficient retrieval. For expiration, consider storing items with timestamps and using lazy deletion or a separate min-heap by expiration time.

3. Implement Store Operation

Iterate from the topmost level downwards to find the first level with available capacity. Insert the item into that level's heap and update the level's current size. If no level has space, either reject or create a new level (depending on requirements).

4. Implement Retrieve Operation

Starting from the topmost level, remove expired items (lazily or eagerly), then check if the level is at least half full. If so, extract the heaviest item from that level's heap and return it. If not, move to the next level. If no level qualifies, return null or an error.

5. Analyze Complexity and Trade-offs

Discuss time and space complexity: store is O(L) where L is number of levels (or O(log n) if using a balanced tree for levels), retrieve is O(L + log n) due to heap operations and expiration checks. Mention alternatives like using a segment tree for level fullness or a global heap with level filtering.

Key Points to Mention

Choice of data structures: max-heap per level for heaviest item retrieval, and possibly a min-heap for expiration management.
Handling expiration: lazy deletion (check on access) vs. eager deletion (periodic cleanup) and their trade-offs.
Definition of 'at least half full': clarify whether it's by item count or total weight, and how that affects the fullness check.
Level selection strategy: always start from topmost level and move down; ensure store places on topmost available level.
Edge cases: empty warehouse, all levels full, all items expired, no level half full, duplicate weights.
Complexity analysis: time for store and retrieve, space usage, and potential optimizations like caching fullness status.

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