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Pinduoduo·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Two-part round at Pinduoduo for a software engineer role. First half was a coding problem around merging intervals, second half was a project walkthrough with follow-up questions. Nothing too wild but the resume deep-dive had some sharp follow-ups I wasn't fully ready for.

Questions Asked (2)

Q1

Given an array of intervals, merge all overlapping ones and return a list of non-overlapping intervals that cover the full input.

Algorithms & Data Structures
Author's notes

Classic problem but they threw a twist on it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying edge cases and constraints, then propose sorting the intervals by start time. Iterate through the sorted list, merging overlapping intervals by comparing the current interval's start with the previous merged interval's end. Return the merged list.

Pro tip: Mention that sorting is the key to achieving O(n log n) time, and explicitly handle edge cases like empty input or single interval. Also, discuss how you would test the solution with examples.

1. Clarify and Confirm

Ask clarifying questions about input format, interval inclusivity, and expected output. Confirm edge cases such as empty array or intervals with same start/end.

2. Sort Intervals

Sort the intervals by their start time. This ensures that any overlapping intervals are adjacent, simplifying the merge process.

3. Merge Overlapping Intervals

Initialize a result list with the first interval. Iterate through the remaining intervals; if the current interval overlaps with the last interval in the result (i.e., its start <= last end), merge them by updating the end to the maximum of both ends. Otherwise, add the current interval to the result.

4. Return Result

After processing all intervals, return the result list containing non-overlapping intervals that cover the input.

5. Analyze Complexity and Test

State the time complexity O(n log n) due to sorting and space complexity O(n) for the output. Walk through a test case to verify correctness.

Key Points to Mention

  • Sorting by start time is crucial for O(n log n) efficiency.
  • Handle edge cases: empty input, single interval, intervals with same start/end.
  • Merge condition: current.start <= last.end (assuming inclusive intervals).
  • Update the end to max(last.end, current.end) when merging.
  • Time complexity: O(n log n) due to sorting; space complexity: O(n) for output.
  • Use a result list and compare with the last interval to avoid unnecessary checks.

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

Q2

Walk me through your most relevant project and be prepared for follow-up questions on the technical and design decisions you made.

Technical Trade-offsSystem Design
Author's notes

I picked a project I knew well but the follow-ups went places I didn't expect.

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AI HintsAI Generated

Suggested Approach

Select a project that closely aligns with the role's requirements and showcases your technical depth. Structure your answer using a clear narrative: context, problem, your specific contributions, technical decisions, and measurable outcomes. Be ready to dive deep into any aspect, explaining trade-offs and alternatives considered.

Pro tip: Proactively mention one thing you would do differently if you had more time or resources, showing self-awareness and a growth mindset. This also steers the conversation toward areas you're prepared to discuss.

1. Set the Context

Briefly describe the project's purpose, your role, team size, and duration. Keep it concise to focus on technical details.

2. Define the Problem

Explain the specific challenge or goal, including any constraints like scalability, latency, or cost. Highlight why it was non-trivial.

3. Detail Your Approach

Walk through your design and implementation choices, emphasizing alternatives considered and why you chose your solution. Use diagrams if helpful.

4. Highlight Technical Decisions

Deep dive into 1-2 key technical decisions, discussing trade-offs (e.g., consistency vs. availability, SQL vs. NoSQL) and how you validated them.

5. Share Results and Learnings

Quantify the impact (e.g., performance improvement, cost reduction) and reflect on what you learned or would change. This shows maturity.

Key Points to Mention

  • Scalability and performance considerations (e.g., handling high traffic, latency optimization)
  • Trade-offs in technology choices (e.g., database selection, caching strategies, microservices vs. monolith)
  • System design principles (e.g., modularity, fault tolerance, load balancing)
  • Collaboration and communication with cross-functional teams
  • Measurable outcomes (e.g., reduced response time by X%, increased throughput by Y%)
  • Lessons learned and future improvements

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