Amazon·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Amazon SWE coding round, one problem the whole session. The question was a warehouse simulation thing with some tricky edge cases around when to skip turns. Felt like it was testing greedy thinking more than anything else.

Questions Asked (1)

You have n warehouses with given inventory levels. Each turn, you dispatch a fixed amount from a warehouse, then your co-worker dispatches their fixed amount. The co-worker can skip their turn up to a limited number of times total, and when they skip, you immediately take another dispatch. You earn a credit for a warehouse only if it hits zero or below right after your dispatch. Design and implement an algorithm that maximizes total credits by choosing optimally when the co-worker skips.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Took me a while to even understand what 'credit' meant here because the problem statement is pretty dense.

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AI HintsAI Generated

Suggested Approach

Model the problem as an optimization over the sequence of skips, recognizing that credits are earned only when a warehouse is reduced to zero or below by your dispatch. Use dynamic programming with state representing remaining skips and current inventory levels, or derive a greedy strategy based on prioritizing warehouses that can be zeroed with minimal skips. Implement and test with brute force for small n to validate.

Pro tip: Clarify the rules with the interviewer before diving in—especially whether the co-worker's dispatch can also reduce inventory and whether skips can be used at any time. This shows attention to detail and avoids solving the wrong problem.

1. Understand the problem and constraints

Restate the problem in your own words, ask clarifying questions about the dispatch amounts, skip mechanics, and credit conditions. Identify the input size to determine the required algorithmic efficiency.

2. Define the objective and state

Formalize the goal: maximize credits by choosing when the co-worker skips. Define a state that captures remaining skips, current inventory levels, and whose turn it is.

3. Explore algorithmic approaches

Consider dynamic programming, greedy, or search strategies. For DP, define transitions for your dispatch, co-worker's dispatch, and skip. For greedy, identify a priority rule (e.g., warehouses closest to zero).

4. Implement and validate

Write clean code for the chosen approach. Test with small cases using brute force to ensure correctness, and analyze time/space complexity.

5. Discuss trade-offs and optimizations

Explain why the chosen approach is optimal or near-optimal. Mention potential improvements, edge cases, and how the solution scales with n and skip limit.

Key Points to Mention

Dynamic programming state definition and transition
Greedy strategy: prioritize warehouses that can be zeroed with few dispatches
Impact of co-worker's dispatches on inventory and credit opportunities
Optimal use of limited skips to chain your dispatches
Complexity analysis (time and space) and scalability
Edge cases: zero inventory, skip limit exhausted, multiple warehouses

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