Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Amazon SWE coding round with a single DP problem that looks deceptively clean until you actually try to implement it. The two-handler job scheduling problem took me a while to see the right state representation.

Questions Asked (1)

You have a list of jobs (each with a type) and two workers. Each worker picks up jobs in the original order, but you can split the list between them however you want. Processing a job costs a 'long' time if the worker's last job was a different type (or they haven't worked yet), and a 'short' time if the last job was the same type. Find the split that minimizes total processing time.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Took me a few minutes to even figure out what state to track.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and define the cost function precisely, then propose a dynamic programming solution that considers all possible split points. Discuss time and space complexity, and explore potential optimizations or alternative approaches.

Pro tip: Demonstrate strong communication by walking through a small example to validate your DP recurrence before coding, and mention how you would test edge cases like all jobs of the same type or alternating types.

1. Understand the problem and constraints

Ask clarifying questions to confirm the cost rules, input size, and whether the split can be empty for one worker. Restate the problem in your own words to ensure alignment.

2. Define the cost function and DP state

Formalize the cost for a worker processing a contiguous subarray, and define a DP state that captures the minimum total cost up to a certain index with the last job type for each worker.

3. Derive the recurrence and base cases

Write the recurrence relation for the DP, considering all possible split points and transitions based on whether the next job matches the worker's last type. Handle base cases for empty prefixes.

4. Analyze complexity and optimize

Compute the time and space complexity of the DP. Discuss potential optimizations, such as reducing state space or using greedy insights if applicable.

5. Validate with examples and edge cases

Walk through a small example to verify the DP works, and test edge cases like all same type, alternating types, or very large input to ensure scalability.

Key Points to Mention

Dynamic programming approach with state representing the split point and last job types for both workers.
Time complexity O(n^2) or better, and space complexity O(n) or O(n^2) depending on implementation.
Handling of base cases: empty list, one worker gets no jobs, or all jobs same type.
Trade-offs between different DP formulations (e.g., tracking last types vs. precomputing costs).
Potential greedy or two-pointer optimizations if the cost function has special properties.
Testing strategy: unit tests for edge cases and performance tests for large inputs.

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