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Early-stage Startup·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Technical phone screen for a software engineer role, one coding problem the whole time. Pretty focused on tree traversal logic dressed up as a job scheduling problem, then a follow-up asking me to critique my own solution.

Questions Asked (2)

Q1

You're given a JSON file of background jobs, each with an id, optional parent_id, and duration_ms. Jobs can spawn up to 15 children recursively. Compute the total cost for each top-level job (its own duration plus all descendants) and return the maximum total cost across all top-level jobs.

Algorithms & Data Structures
Author's notes

Took me a minute to see past the job scheduling framing and realize this is just a tree problem.

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AI HintsAI Generated

Suggested Approach

First, clarify the input format and constraints, then propose building a tree from the parent-child relationships and computing subtree sums via post-order traversal. Finally, identify top-level jobs (those without a parent) and return the maximum of their subtree sums.

Pro tip: Mention that you would handle potential cycles or missing parents defensively, and discuss the trade-offs between recursive and iterative traversal to avoid stack overflow with deep trees.

1. Clarify requirements and edge cases

Ask about input size, whether the JSON is guaranteed to be a valid tree, and if there can be multiple roots or orphan nodes. Confirm that 'top-level' means jobs with no parent_id.

2. Choose data structures

Build a hash map from job id to job object for O(1) lookup, and an adjacency list mapping parent_id to children. This enables efficient traversal.

3. Compute subtree sums

Perform a post-order traversal (DFS) starting from each top-level job, summing durations of all descendants. Use recursion or an explicit stack to avoid recursion depth issues.

4. Track maximum total cost

While computing sums, keep a running maximum of the total cost for each top-level job. Return that maximum after processing all roots.

5. Analyze complexity and test

State that the solution is O(n) time and O(n) space. Walk through a small example to verify correctness, including edge cases like a single job or a deep chain.

Key Points to Mention

  • Tree construction from parent-child relationships using a hash map and adjacency list
  • Post-order traversal to accumulate durations from children to parent
  • Identification of top-level jobs as those with no parent_id
  • Handling of edge cases: empty input, multiple roots, orphan nodes, cycles
  • Time and space complexity: O(n) time, O(n) space
  • Trade-offs between recursive and iterative DFS for deep trees

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

Q2

What improvements or optimizations would you make to your solution?

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Blanked a bit here.

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AI HintsAI Generated

Suggested Approach

Acknowledge the current solution's strengths and limitations, then propose specific optimizations prioritized by impact and effort. Frame improvements in terms of trade-offs relevant to an early-stage startup, such as development speed, scalability, and maintainability.

Pro tip: Quantify the expected impact of each optimization (e.g., 'reduces time complexity from O(n^2) to O(n log n)') and tie it to business outcomes like user experience or infrastructure cost. This shows you think like a product-minded engineer, not just a coder.

1. Summarize current solution

Briefly restate the key aspects of your solution and its performance characteristics, acknowledging any known limitations.

2. Identify improvement areas

List potential areas for optimization, such as algorithmic efficiency, code readability, scalability, or resource usage.

3. Prioritize by impact and effort

Rank the improvements based on their potential impact and the effort required, considering startup constraints like time and resources.

4. Propose specific optimizations

Detail the top 1-2 optimizations, explaining how you would implement them and the expected benefits.

5. Discuss trade-offs and next steps

Acknowledge any trade-offs (e.g., added complexity, testing overhead) and suggest how you would validate the improvements.

Key Points to Mention

  • Time and space complexity improvements (e.g., using a hash map to reduce lookup time)
  • Code maintainability and readability (e.g., modularization, naming conventions)
  • Scalability considerations (e.g., handling increased data volume or concurrent users)
  • Resource optimization (e.g., memory usage, CPU cycles)
  • Trade-offs between optimization and development speed
  • Testing and validation strategy for the proposed changes

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