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Anthropic·Software Engineer·Onsite - Coding / Algorithms·Senior

Senior
Jul 2026

Summary

Coding round at Anthropic for a software engineer role. Started with a single-threaded web crawler and then got pushed into a multi-threaded extension with a bunch of concurrency discussion. Pretty solid round if you're comfortable with systems-adjacent coding problems.

Questions Asked (2)

Q1

Implement a single-threaded web crawler starting from a seed URL, using BFS to follow links, deduplicating visited pages, and handling basic URL normalization.

Algorithms & Data StructuresSystem Design
Author's notes

The BFS part came naturally but I spent probably too long bikeshedding over URL normalization edge cases before writing any actual code.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline the BFS algorithm with a queue and a visited set. Discuss URL normalization rules and how to handle edge cases like redirects and relative links. Finally, analyze time and space complexity and potential improvements.

Pro tip: Mention that you would use a set for O(1) visited checks and normalize URLs by lowercasing the scheme and host, removing default ports, and resolving relative paths. Also, discuss politeness policies like robots.txt and rate limiting, even though not required, to show production awareness.

1. Clarify Requirements

Ask about scope: single-threaded, BFS, deduplication, URL normalization. Confirm expected scale, error handling, and whether to respect robots.txt.

2. Design Data Structures

Use a queue for BFS and a set for visited URLs. Explain that the set enables O(1) lookup for deduplication.

3. Implement URL Normalization

Describe normalization steps: lowercase scheme/host, remove default ports, resolve dot segments, sort query parameters, and strip fragments.

4. Outline BFS Crawling Logic

Initialize queue with seed URL, mark as visited. While queue not empty, dequeue URL, fetch page, extract links, normalize each, and enqueue if not visited.

5. Analyze and Optimize

Discuss time complexity O(N) where N is number of pages, space O(N) for queue and visited set. Mention potential improvements like concurrent crawling or distributed crawling.

Key Points to Mention

  • BFS ensures level-by-level crawling and avoids deep recursion.
  • Visited set prevents infinite loops and duplicate processing.
  • URL normalization: lowercasing scheme/host, removing default ports, resolving relative paths, sorting query params, stripping fragments.
  • Handling relative URLs by joining with base URL.
  • Error handling for network failures, timeouts, and non-HTML content.
  • Politeness: respecting robots.txt and rate limiting (even if not required, shows maturity).

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

Q2

Extend your crawler to run with multiple threads. How do you synchronize the visited set, design the work queue, size the thread pool, and handle the difference between I/O-bound and CPU-bound work?

System DesignTechnical Trade-offs
Author's notes

This is where it got interesting.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the crawler's architecture and constraints, then systematically address each component: synchronization of the visited set, work queue design, thread pool sizing, and the I/O vs CPU-bound distinction. Emphasize trade-offs and justify your choices with concrete reasoning, showing awareness of scalability and correctness.

Pro tip: Mention that Python's GIL makes threading ineffective for CPU-bound work, so you'd use multiprocessing or async I/O for I/O-bound tasks—this shows you understand the practical limits of threading in real systems.

1. Clarify requirements and constraints

Ask about the crawler's scale, target sites, politeness policies, and whether it's I/O or CPU intensive. This sets the stage for tailored design decisions.

2. Synchronize the visited set

Use a thread-safe data structure like a concurrent set or a lock-protected set. Consider using a database or distributed cache for large-scale crawlers to avoid memory bottlenecks.

3. Design the work queue

Implement a thread-safe queue (e.g., queue.Queue in Python) with blocking operations to avoid busy-waiting. Ensure it supports dynamic addition of URLs and graceful shutdown.

4. Size the thread pool

For I/O-bound tasks, use a larger pool (e.g., 2-5x CPU cores) to overlap waiting; for CPU-bound tasks, match the number of cores to minimize context switching. Benchmark to find the optimal size.

5. Handle I/O vs CPU-bound work

Use threading or async I/O for I/O-bound tasks to maximize concurrency; use multiprocessing or native threads for CPU-bound tasks. Consider hybrid approaches or separate pools for different stages.

Key Points to Mention

  • Thread-safe data structures: locks, concurrent collections, or atomic operations for the visited set.
  • Work queue design: blocking queue, backpressure, and dynamic URL addition.
  • Thread pool sizing: formula based on I/O wait time vs CPU time (e.g., N_threads = N_cores * (1 + wait_time/compute_time)).
  • I/O-bound vs CPU-bound: threading benefits I/O-bound, multiprocessing benefits CPU-bound; GIL limitations in Python.
  • Scalability and fault tolerance: handling failures, retries, and distributed crawling.
  • Politeness and rate limiting: respecting robots.txt and avoiding overwhelming servers.

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