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Anthropic·Software Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Anthropic software engineer interview with a warm-up coding round where you pick your poison: web crawler or image processing. Pretty open-ended, which sounds nice until you realize you have to drive the whole thing yourself.

Questions Asked (1)

Q1

You're given a choice: implement a web crawler starting from a URL (fetching pages, extracting links, staying on the same domain, no revisits) OR implement an image processing routine over a 2D pixel grid (flood fill, region counting, boundary detection, or convolution). Pick one and code it end-to-end, then discuss how you'd extend it.

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

I went with the web crawler because I figured I had more to say about it.

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AI HintsAI Generated

Suggested Approach

Choose the problem you can implement most cleanly and completely within the time limit, then state your choice and rationale upfront. For a web crawler, outline BFS with a queue and visited set, domain filtering, and error handling; for image processing, outline BFS/DFS flood fill or convolution with boundary checks. After coding, discuss extensions like concurrency, distributed crawling, or performance optimizations.

Pro tip: Pick the web crawler if you're comfortable with networking and concurrency, as it naturally leads to richer system design discussions; pick image processing if you want a self-contained algorithmic problem with clear correctness criteria. Either way, explicitly state your assumptions (e.g., robots.txt, pixel connectivity) before coding.

1. Clarify requirements and choose

Ask clarifying questions about scope, constraints, and expected behavior. Then choose the problem that best showcases your strengths and fits the time.

2. Outline the algorithm

Sketch the core algorithm with data structures (e.g., queue for BFS, visited set, 2D array for pixels) and discuss time/space complexity.

3. Implement end-to-end

Write clean, modular code with error handling and edge cases. For crawler: handle relative URLs, same-domain check, and HTTP errors. For image: handle boundaries, connectivity, and in-place vs new grid.

4. Test with examples

Walk through a small example to verify correctness, including edge cases like cycles, empty inputs, or single-pixel regions.

5. Discuss extensions

Propose improvements: for crawler, add concurrency, politeness, distributed crawling, or storage; for image, add optimizations, larger images, or GPU acceleration.

Key Points to Mention

  • BFS vs DFS trade-offs for crawling or flood fill, and why BFS is often preferred for shortest-path or level-order processing.
  • Handling cycles and avoiding infinite loops via a visited set or marking pixels.
  • Domain restriction and URL normalization for web crawler; connectivity (4 vs 8) and boundary conditions for image processing.
  • Time and space complexity analysis for the chosen algorithm.
  • Error handling and robustness (e.g., network failures, malformed URLs, out-of-bounds pixels).
  • Scalability extensions: concurrency, distributed systems, or performance optimizations like memoization or parallel processing.

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