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Anthropic·Machine Learning Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Anthropic ML engineer interview with a pretty gnarly coding question about converting sampling profiler output into a visualization-friendly trace of start/end events. The problem had a lot of edge cases to reason through and required both clean implementation and a discussion of design decisions.

Questions Asked (1)

Q1

Given a list of sampling profiler samples (each with a timestamp and a call stack), implement a function that converts them into a sequence of start/end trace events suitable for visualization. Use the longest common prefix between consecutive stacks to determine which frames ended and which started. Also handle the first sample, the last sample, duplicate function names at different depths, empty stacks, equal timestamps, and optionally a synthetic root frame. Provide time and space complexity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

This one took me a while to even parse.

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AI HintsAI Generated

Suggested Approach

Clarify requirements and edge cases, then describe an algorithm that processes samples in order, maintaining a stack of active frames and using the longest common prefix between consecutive stacks to emit end events for frames that disappear and start events for new frames. Walk through edge cases and analyze complexity, emphasizing how the approach handles duplicate names, empty stacks, equal timestamps, and optional synthetic root.

Pro tip: Explicitly state that you use frame identity (e.g., a unique ID per stack entry) rather than function name to match frames, which prevents bugs with recursion or duplicate names at different depths. Also, mention that you can handle equal timestamps by emitting zero-duration events or by ordering events carefully to avoid negative durations.

1. Clarify requirements and edge cases

Ask about the expected output format, whether a synthetic root is needed, and how to handle empty stacks, equal timestamps, and duplicate function names. Confirm that the longest common prefix is computed between consecutive stacks.

2. Design the algorithm

Process samples in chronological order. For each sample, compute the longest common prefix with the previous stack. Emit end events for frames in the previous stack beyond the prefix (in reverse order), then start events for new frames in the current stack beyond the prefix. Handle the first sample by starting all frames from the root (or synthetic root) and the last sample by ending all remaining frames.

3. Address edge cases

For empty stacks, treat as an empty call stack and ensure all previous frames are ended. For equal timestamps, emit events with zero duration or adjust ordering to maintain non-negative durations. For duplicate function names, use frame identity (e.g., a unique ID per stack entry) to correctly match frames.

4. Analyze complexity

Time complexity is O(N * D) where N is the number of samples and D is the maximum stack depth, as each frame is processed at most twice (start and end). Space complexity is O(D) for the active stack, plus O(N * D) for the output events in the worst case.

5. Discuss trade-offs and optimizations

Mention that using a stack of frame IDs allows O(1) comparison of frames. Discuss whether to include a synthetic root and how it affects visualization. Consider if timestamps are monotonic and how to handle out-of-order samples.

Key Points to Mention

  • Longest common prefix between consecutive stacks determines which frames ended and which started.
  • Use frame identity (unique ID) rather than function name to handle duplicates and recursion.
  • Handle first sample by starting all frames from root (or synthetic root) and last sample by ending all frames.
  • Empty stacks: end all active frames; equal timestamps: emit zero-duration events or adjust ordering.
  • Time complexity O(N*D), space complexity O(D) for active stack plus output size.
  • Optional synthetic root frame can simplify visualization and ensure a single root.

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