Anthropic Interview Questions (2026)
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1/5anthropic fellows two-round codesignal online assessment review for machine learning intern
Anthropic Technical Interview (55 min CodeSignal) – Anyone done this before?
Design a 1-to-1 Chat System
Task Management System (Online Assessment)
Banking System (Online Assessment)
Web Crawler
Employee Management System (Online Assessment)
Inference API System Design
Prompt Playground System Design
Recipe Manager (Online Assessment)
Distributed Model Deployment System Design
Deduplicate Files
Tokenize (Python)
LLM Request Batching API System Design
Converting Stack Samples to Trace Events
Distributed Mode and Median
In-memory Database (Online Assessment)
Anthropic Onsite Interview: In-Memory Cache Extension Coding Challenge
Interview Question
Implement an LRU Cache
High-Concurrency Prompt Template Deduplication (Array + Hash Map)
Thread-Safe Linked List Task Queue Transformation
Task Management System (CRUD + Priority Ordering + User Quota + History Query)
Multi-threaded Web Crawler with URL De-duplication
Web Crawler with Asyncio
anthropic fellows two-round codesignal online assessment review for machine learning intern
Question Details
分享一下 人类学Fellows 的两轮 CodeSignal 面经,希望对后面申请的人有帮助
四月中申请的前几天收到, 第一关做完后马上收到第二关于是接着给他做完了
具体time line 如下
然后都是proctored开摄像头麦克风
以下内容需要积分高于 188 您已经可以浏览
第一轮是 90 分钟的 general coding assessment,我遇到的是一个偏系统/网络模拟的 Python 题,大方向是实现一个简化版的 DNS resolver。题目会分成多个 step,每一步对应一组 unit tests,需要逐步实现功能。整体不是传统 LeetCode 那种纯算法题,更像是给你一个 mini codebase 和清晰 spec,然后要求你按测试一步步补全逻辑。
难度体感上,我觉得第一轮大概是 LeetCode Medium 左右,但不是算法难,而是工程逻辑和边界处理比较多。前面几步比较 straightforward,比如字符串 normalize、基本递归/迭代查询;后面会逐渐加入更多情况,例如 alias / fallback / error handling / cycle handling / caching / concurrency 等。然后得保持代码结构清楚,不然后面 step 叠加功能时容易把前面的测试弄挂。
第二轮是 60 分钟 Python debugging assessment。这个和一般 OA 很不一样,不是从零写代码,而是给你一个已经存在的 codebase,里面有一些 failing unit tests,需要你读代码、读 tests,然后修 root cause。
debugging 的话题目 没啥好拍的, codebase太长了没拍
难度体感上,第二轮比第一轮更tricky主要因为时间短,而且你需要快速理解别人写好的代码。如果平时只刷 LeetCode,可能会不太适应。不过平常numpy 熟悉的话难度应该中等 , 主要就是Python debugging + unit test-driven development。我觉得难点主要在三点:第一是快速定位 root cause;第二是不要为了一个 test 过度修改导致其他 tests 挂掉;第三是有些 bug 不只是返回值错,也可能是状态、调用顺序、缓存、随机性或 mutation 行为不符合测试预期。
然后最后第二题在时间结束前3分钟成功做完, 但看地里帖子 据说先做完还会加分??
大概就这样,麻烦各位给点米 T_T
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Anthropic Interview Process Overview
The Anthropic interview process typically includes a recruiter screen, one to two technical phone screens, and a 4-6 round on-site or virtual on-site loop. Each round serves a distinct calibration purpose: coding rounds measure correctness, code quality, and complexity reasoning; system design rounds measure architectural judgment at the appropriate level; behavioral rounds measure ownership, leadership scope, and collaboration. Reports tagged on LeakCode from 2024-2026 show Anthropic runs a calibrated process consistent with industry norms for companies of its tier.
Difficulty calibration: Anthropic coding rounds typically run medium difficulty with follow-up depth as the senior discriminator. System design rounds expect production-grade trade-off articulation at L4+ levels. Behavioral rounds expect quantified outcomes ("reduced p99 latency from 800ms to 120ms") rather than vague impact claims. The candidates who advance consistently demonstrate clear thinking out loud rather than perfect final answers.
How To Use Anthropic Question Reports
Real candidate-reported interview questions are a calibration tool, not a memorization target. Anthropic updates its question pool every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage approach: identify the patterns that appear repeatedly in Anthropic reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions above by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Anthropic's pool. Reports tagged with quantified difficulty and explicit round type are higher-signal than reports without those tags. The metadata filters help you build a focused study plan in 1-2 hours rather than 8-10 hours of unstructured browsing.
Common Anthropic Interview Mistakes
Reports tagged "no hire" at Anthropic consistently surface a few patterns: jumping into code without clarifying requirements, coding silently for extended periods, missing edge cases (empty input, single element, large input, overflow), producing working code the candidate cannot refactor when probed, and behavioral stories that use "we" instead of "I" diluting individual signal. Strong candidates explicitly avoid these patterns by following a consistent round template.
The single most predictive failure mode in recent reports: not asking clarifying questions. Interviewers are explicitly trained to weight this dimension. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into implementation immediately. Strong candidates also verbalize their approach before writing code; weak candidates code in silence and lose the communication dimension of the round's calibration.
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