Anthropic Interview Questions

Anthropic Interview Questions

Practice 182 real Anthropic interview questions for 2026. Covers all top categories — Coding & Algorithms, System Design, Behavioral & Leadership, ML System Design, Software Engineering Fundamentals — across Software Engineer, Machine Learning Engineer, and Backend Engineer roles. Real questions from actual interviews with detailed solutions. Use this guide for focused interview preparation that prioritizes coding and systems work first, then ML/analysis and behavioral alignment rounds. Expect a multi-stage loop: a 30–45 minute recruiter screen, one or two technical screens (live coding or take-home, 45–90 minutes), an in-depth system-design round, values/mission-alignment conversations, and a final team-match/hiring-committee step — most loops finish in about three to six weeks. Software Engineer questions skew toward distributed systems and storage (distributed rate limiter, LLM request batching, in-memory/time-based key-value designs), web crawl and indexing problems, and algorithmic puzzles (LRU, unique-character subsets, grid word search). Machine Learning Engineer rounds emphasize production inference, routing and scheduling, batch inference design, experiment design (double descent) and model distribution. Anthropic explicitly evaluates AI-collaboration judgment in some coding rounds and often allows AI assistants; difficulty is comparable to other top AI labs but with stronger emphasis on safety, judgment, and ML-systems thinking.

182 Questions 1 Company07.30.2026
Showing 20 results
Role
Anthropic logo
Anthropic
Medium
Software Engineer

Implement a same-host web crawler

Implement a web crawler. Given a start URL and a function get_links(url) -> list of URLs, return all URLs that share the same hostname as the start. A...

Coding & Algorithms
14
0
102 people solved
Aug 1, 2025
Anthropic logo
Anthropic
Medium
Software Engineer

Detect duplicate files by content

Detect duplicate files by content in a filesystem. Given access to a directory tree, return groups of file paths that have identical byte content. Min...

Coding & Algorithms
12
0
188 people solved
Aug 1, 2025
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Anthropic
Hard
Software Engineer Locked

Optimize a core kernel for throughput

This question evaluates low-level performance optimization competencies, including loop transformations, memory-access patterns, vectorization, parall...

Software Engineering Fundamentals
16
0
205 people solved
Nov 19, 2025
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Anthropic
Hard
Software Engineer

Implement staircase printing and distributed mode/median

Problem A: Print a “staircase” pattern Implement a function that prints a staircase with n rows. Input - An integer n (n >= 1) Output - Print n lines....

Coding & Algorithms
67
0
923 people solved
Nov 19, 2025
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Anthropic
Medium
Software Engineer Locked

Implement crawler and file deduplication

This question evaluates competencies in concurrent programming, web crawling and graph traversal, thread safety, rate limiting, I/O-efficient file ded...

Coding & Algorithms
11
0
97 people solved
Feb 28, 2026
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Anthropic
Medium
Software Engineer

Generate outputs for images and pipelines

You are given m input images and n processing pipelines. - Each pipeline is an ordered list of k operations (e.g., resize, rotate, crop, blur, color t...

Coding & Algorithms
114
0
1241 people solved
Nov 10, 2025
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Anthropic
Medium
Software Engineer

Implement Python LRU cache with args and persistence

Implement an LRU cache in Python as a decorator or class that correctly supports variable-length positional arguments and keyword arguments. Ensure th...

Coding & Algorithms
46
0
314 people solved
Aug 1, 2025
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Anthropic
Medium
Machine Learning Engineer Locked

Implement a web crawler using a provided API

This question evaluates understanding of web crawling mechanics, URL/hostname filtering, graph traversal concepts, and concurrent fetching, assessing ...

Coding & Algorithms
35
0
323 people solved
Feb 11, 2026
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Anthropic
Hard
Software Engineer Locked

Build a least-recently-used cache

This question evaluates a candidate's competence in designing and implementing efficient in-memory cache mechanisms, focusing on data structures, evic...

Coding & Algorithms
4
0
35 people solved
Feb 8, 2026
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Anthropic
Hard
Software Engineer

Find duplicate files and apply image operations

Part A — Find duplicate files by content You are given a list of directory records. Each record is a string describing a directory path followed by on...

Coding & Algorithms
115
0
771 people solved
Oct 15, 2025
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Anthropic
Medium
Software Engineer

Implement file deduplication at scale

Write a program to deduplicate files in a very large directory tree. Identify groups of identical files without loading entire files into memory. Outl...

Coding & Algorithms
99
0
758 people solved
Sep 6, 2025
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Anthropic
Medium
Software Engineer Locked

Design an IPv4 Address Iterator

This question evaluates a candidate's ability to represent and iterate IPv4 addresses, covering parsing, numeric arithmetic with carry between octets,...

Coding & Algorithms
15
0
111 people solved
Jan 10, 2026
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Anthropic
Hard
Software Engineer

Crawl Same-Domain Links

Implement a Python function that crawls a website starting from a seed URL and returns all unique pages reachable within the same domain. Assume a hel...

Coding & Algorithms
16
0
114 people solved
Jan 6, 2026
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Anthropic
Medium
Software Engineer Locked

Implement a longest-match tokenizer

This question evaluates string-processing and algorithmic implementation skills for greedy longest-match tokenization, including handling runs of unma...

Coding & Algorithms
4
0
70 people solved
Oct 19, 2025
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Anthropic
Medium
Software Engineer

Design and implement a Python solution

Design and implement a solution in Python for a problem specified by the interviewer. Outline your approach and chosen data structures, justify key de...

Coding & Algorithms
26
0
243 people solved
Sep 6, 2025
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Anthropic
Medium
Software Engineer

Apply bitwise tricks for performance

For an integer-heavy inner loop, propose bit-level optimizations that reduce branches and memory traffic: e.g., population count usage, fast modulo by...

Coding & Algorithms
22
0
162 people solved
Sep 6, 2025
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Anthropic
Medium
Software Engineer

Compute matrix trace and support updates

Given an n x n integer matrix M, implement a function trace (M) that returns the sum of its main diagonal. Follow-ups: (a) If you receive q point upda...

Coding & Algorithms
15
0
185 people solved
Aug 13, 2025
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Anthropic
Medium
Software Engineer

Detect n-length consecutive sequences

Given an unsorted collection of integers and a parameter n > 0, determine whether there exists a set of n distinct integers that form a run of consecu...

Coding & Algorithms
23
0
216 people solved
Aug 13, 2025
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Anthropic
Medium
Software Engineer

Implement thread-safe blocking queue

Question Design and implement a thread-safe bounded blocking queue that supports concurrent enqueue and dequeue operations using standard synchronizat...

Coding & Algorithms
8
0
13 people solved
Jul 29, 2025
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Anthropic
Hard
Software Engineer

Implement a Longest-Match Tokenizer

Implement a Longest-Match Tokenizer The source reports longest matching, unknown-token handling, and an optional unknown-merging extension. The exact ...

Coding & Algorithms
2
0
18 people solved
Jul 8, 2026

Frequently Asked Questions

How difficult are Anthropic interview questions compared with other AI labs and FAANG-style companies?
Anthropic interviews are generally rated as challenging and tend to sit between typical FAANG SWE loops and specialized ML-systems lab screens. Expect coding rounds that reward practical, production-ready code rather than contrived LeetCode puzzles, and system-level questions that probe distributed inference, batching, and safety tradeoffs. Machine learning engineering interviews emphasize ML systems, inference routing and experiment design more than pure theoretical ML. Overall difficulty is comparable to top AI labs: high bar for systems thinking, scalability, and alignment awareness, but slightly less focused on pure algorithmic trickery and more on design, engineering rigor, and judgment.
What is the Anthropic interview process, stage by stage, and where do Anthropic interview questions typically appear?
The process runs stage by stage: a 30-minute recruiter screen to confirm background and mission fit; one or two technical screens (60–90 minutes) that may be live coding or platform-based; a take-home or focused work sample followed by a discussion; an onsite or virtual loop of 4–6 interviews covering coding, system and ML system design, and values/mission alignment; then team matching, hiring committee review, and offer negotiation. Total timeline commonly runs three to six weeks. Questions appear across Coding & Algorithms, System Design, ML System Design, Software Engineering Fundamentals, and Behavioral & Leadership rounds, distributed by role.
How should I structure my preparation timeline for Anthropic interviews?
Plan 4–8 weeks of focused prep. Weeks 1–2, refresh core data structures and algorithm patterns with timed practice and small production-style problems; explicitly practice writing modular, testable code and clear tests. Weeks 3–4, run system-design drills for distributed rate limiting, batching, and inference-serving scenarios; build short design write-ups you can present. Weeks 5–6, practice ML-systems topics: inference routing, batch vs. streaming, experiments, and scaling; rehearse behavioral stories aligned to AI-safety and mission. Throughout, do mock interviews that include explicit discussion of AI-tool use and decision-making.
What key technical subtopics should I focus on for Software Engineer, Machine Learning Engineer, and Backend roles at Anthropic?
For Software Engineer candidates, expect recurring themes in distributed systems and production services: distributed rate limiting, LLM request batching, in-memory key-value stores, nested transactions, caching (LRU), and product-style system design like a prompt playground or feed. Algorithmic problems include grid searches, unique-character subsets, and path resolution. Machine Learning Engineer interviews center on ML infrastructure and experiments: production model serving, batch inference and scheduling, inference routing, model downloaders, experiment design such as double-descent, and converting state streams to events. Backend questions also emphasize alignment with team needs and handling interview misalignment when it arises.
What standout tips and common pitfalls should I know before interviewing at Anthropic?
Standout tips: demonstrate production-minded code, show end-to-end design thinking, and explicitly surface safety and alignment tradeoffs. If a round permits AI-assistant use, demonstrate careful, transparent judgment about what you asked the assistant, why, and how you validated its output. Use concrete performance numbers and failure modes in designs, and practice succinct STAR stories tied to mission-driven impact. Common pitfalls include over-relying on AI without attribution, failing to state assumptions or test cases, glossing over scalability and edge cases, and underpreparing for values and mission-alignment conversations.

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