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Anthropic·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Jun 2026Remote

Summary

Coding round at Anthropic for a software engineer role, focused on graph traversal with a real system design twist baked in. The multithreaded follow-up is where things got interesting.

Questions Asked (2)

Q1

Implement a single-domain web crawler: given a starting URL and an interface that fetches all linked URLs from a page, return every reachable URL that shares the same hostname as the start URL, with no duplicates.

Algorithms & Data StructuresSystem Design
Author's notes

BFS felt like the obvious move so I went with that, queue plus a visited set.

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AI HintsAI Generated

Suggested Approach

Use BFS with a queue and a visited set to explore all reachable URLs, filtering by hostname. For each URL, fetch its linked URLs, add unvisited same-host URLs to the queue, and continue until the queue is empty. Return the visited set as the result.

Pro tip: Clarify the interface's behavior (e.g., whether it returns absolute or relative URLs) and discuss handling edge cases like redirects, non-HTML content, and rate limiting to show production awareness.

1. Clarify requirements and constraints

Ask about the fetch interface's return format, expected scale, and whether concurrency or politeness policies are needed. Confirm that only URLs with the same hostname as the start URL should be returned.

2. Choose BFS with visited set

Explain that BFS ensures all reachable URLs are found, and a visited set prevents duplicates and infinite loops. Normalize URLs (e.g., resolve relative paths, strip fragments) before adding to the set.

3. Outline the algorithm

Initialize a queue with the start URL and a visited set containing it. While the queue is not empty, dequeue a URL, fetch its linked URLs, and for each link that is same-host and not visited, add to visited and enqueue.

4. Discuss edge cases and optimizations

Mention handling of redirects, non-HTML pages, and errors. For large sites, discuss concurrency, rate limiting, and using a distributed queue or Bloom filter for scalability.

5. Analyze complexity and trade-offs

State that time complexity is O(N) where N is the number of URLs, and space is O(N) for the visited set and queue. Discuss trade-offs between BFS and DFS, and between in-memory vs. external storage.

Key Points to Mention

  • BFS traversal with a queue and visited set to avoid duplicates and cycles
  • URL normalization (resolving relative URLs, removing fragments, handling case sensitivity)
  • Hostname comparison: exact match vs. subdomain handling
  • Concurrency and rate limiting for politeness and performance
  • Error handling for network failures, timeouts, and non-HTML content
  • Scalability considerations: distributed crawling, Bloom filters, and external storage

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

Q2

How would you parallelize the crawler using a thread pool, and what are the key concerns around concurrency, shared state, and knowing when the crawl is actually done?

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This is the part I wasn't fully ready for.

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AI HintsAI Generated

Suggested Approach

Start by outlining a thread pool architecture with a shared work queue and a thread-safe visited set, then dive into concurrency concerns like race conditions and deadlocks, and finally explain termination detection using atomic counters or a sentinel. Emphasize trade-offs between throughput, correctness, and resource usage.

Pro tip: Mention that Python's GIL limits CPU-bound parallelism but I/O-bound crawling benefits from threads; alternatively, suggest asyncio for higher concurrency. Also, highlight the importance of backpressure to avoid overwhelming the queue or target servers.

1. Design the Thread Pool Architecture

Describe a fixed-size thread pool with a shared queue of URLs to crawl. Each worker thread pops a URL, fetches the page, extracts links, and enqueues new URLs if not visited.

2. Ensure Thread-Safe Shared State

Use locks or concurrent data structures for the visited set and the work queue. Discuss options like mutexes, read-write locks, or lock-free structures, and the trade-offs.

3. Handle Concurrency Concerns

Address race conditions (e.g., duplicate URL enqueue), deadlocks (lock ordering), and resource contention (network, memory). Mention rate limiting and politeness policies.

4. Detect Crawl Completion

Explain termination detection: track active tasks with an atomic counter; when the queue is empty and no tasks are active, signal completion. Alternatively, use a sentinel value or a condition variable.

5. Discuss Trade-offs and Alternatives

Compare threads vs. asyncio vs. multiprocessing. Discuss scalability, GIL limitations, and error handling (retries, timeouts).

Key Points to Mention

  • Thread pool with a bounded queue and worker threads to control concurrency.
  • Thread-safe visited set using locks or concurrent data structures (e.g., ConcurrentHashMap, synchronized set).
  • Atomic counters or a task tracker to know when all work is done (e.g., queue empty and no active workers).
  • Race conditions: duplicate URL processing, lost updates; use double-checked locking or atomic operations.
  • Deadlock avoidance: consistent lock ordering, timeouts on locks.
  • Backpressure and rate limiting to avoid overwhelming the system or target servers.

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