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
Hard
Software Engineer Locked

Improve concurrency beyond a single lock

This question evaluates mastery of concurrent programming and synchronization concepts, focusing on correctness versus performance trade-offs when pro...

Software Engineering Fundamentals
27
0
308 people solved
Feb 1, 2026
Anthropic logo
Anthropic
Medium
Software Engineer Locked

LRU Cache

This question evaluates a candidate's ability to design a data structure combining a hash map with a doubly linked list to achieve constant-time acces...

Coding & Algorithms
2
0
12 people solved
Jun 23, 2026
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Anthropic
Medium
Software Engineer AI Locked

Maximum-Length Unique-Character Subset

This question evaluates a candidate's ability to combine bitmasking with backtracking or subset enumeration to maximize a constraint over combinations...

Coding & Algorithms
0
0
7 people solved
Jun 23, 2026
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Anthropic
Medium
Software Engineer Locked

Word Search in a Letter Grid

This question evaluates a candidate's ability to search for multiple target words within a grid of letters using depth-first traversal and backtrackin...

Coding & Algorithms
0
0
13 people solved
Jun 23, 2026
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Anthropic
Medium
Software Engineer Locked

Same-Domain Web Crawl (BFS)

This question evaluates a candidate's ability to implement graph traversal using breadth-first search while applying a domain-matching filter and dedu...

Coding & Algorithms
0
0
7 people solved
Jun 23, 2026
Anthropic logo
Anthropic
Hard
Software Engineer

Design a concurrent web crawler

Question Design and implement a concurrent web crawler. Starting from one or more seed URLs, the crawler should fetch pages, extract links, deduplicat...

System Design
29
0
200 people solved
Sep 6, 2025
Anthropic logo
Anthropic
Hard
Machine Learning Engineer

Design a scalable MapReduce pipeline

Design a scalable MapReduce pipeline Design a Large-Scale MapReduce-Style Data Processing System Context You are designing a batch pipeline, using a M...

System Design
29
0
229 people solved
Aug 1, 2025
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Anthropic
Hard
Machine Learning Engineer

Demonstrate culture fit and leadership

Behavioral & Leadership — Machine Learning Engineer (Onsite) Instructions Answer concisely using the STAR framework (Situation, Task, Actions, Results...

Behavioral & Leadership
26
0
225 people solved
Aug 14, 2025
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Anthropic
Hard
Software Engineer Locked

Implement a hierarchical file store

This question evaluates proficiency in modeling hierarchical file systems, path parsing and manipulation, managing mutable file contents, and analyzin...

Coding & Algorithms
4
0
19 people solved
May 12, 2026
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Anthropic
Hard
Machine Learning Engineer

Explain management style, execution strategy, and culture choices

Behavioral & Leadership: ML Engineering Onsite Context You are interviewing for a Machine Learning Engineer role with significant leadership responsib...

Behavioral & Leadership
13
0
174 people solved
Sep 6, 2025
Anthropic logo
Anthropic
Medium
Software Engineer Locked

Build a concurrent web crawler

This question evaluates understanding of concurrent programming, synchronization, thread safety, and graph traversal as applied to a multithreaded web...

Coding & Algorithms
22
0
144 people solved
Apr 5, 2026
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Anthropic
Medium
Software Engineer

Find Duplicate Files

Implement a file deduplication tool. You are given a root directory containing many files. Return groups of duplicate files. Two files are duplicates ...

Coding & Algorithms
12
0
79 people solved
Mar 2, 2026
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Anthropic
Medium
Software Engineer

Detect duplicate files efficiently

You are given access to a very large file system containing file paths and read access to file contents. Design an algorithm to identify groups of fil...

Coding & Algorithms
46
0
815 people solved
Sep 6, 2025
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Anthropic
Medium
Software Engineer

Design a single- and multi-threaded web crawler

Web Crawler (single-threaded, then multi-threaded) You are given: - A starting URL startUrl (e.g., "http://news.example.com/a/index.html"). - An inter...

Coding & Algorithms
64
0
520 people solved
Oct 26, 2025
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Anthropic
Hard
Software Engineer

Optimize a compute kernel with a simulator

Performance Optimization Plan for a Compute Kernel Context You are given: - A compute kernel (single critical function or set of loops) to optimize. -...

System Design
17
0
229 people solved
Sep 6, 2025
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Anthropic
Medium
Software Engineer Locked

Implement an in-memory DB with TTL backup/restore

This question evaluates implementation and reasoning skills for an in-memory nested key→field→value store with TTL-based expirations, snapshot backup/...

Coding & Algorithms
79
2
873 people solved
Feb 11, 2026
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Anthropic
Medium
Software Engineer Locked

Design a prompt-sharing platform

This question evaluates system design competencies including product and backend architecture, data modeling and indexing, permission and sharing mode...

System Design
22
0
237 people solved
Oct 19, 2025
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Anthropic
Medium
Software Engineer Locked

Optimize HTTP requests for speed and limits

This question evaluates understanding of concurrency, throughput optimization, rate limiting, and trade-offs in designing scalable networked services....

System Design
34
0
454 people solved
Dec 8, 2025
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Anthropic
Medium
Software Engineer Locked

How would you scale batch image pipelines?

This question evaluates a candidate's competence in designing scalable, reliable batch image-processing pipelines, testing knowledge of distributed sy...

System Design
26
0
478 people solved
Nov 10, 2025
Anthropic logo
Anthropic
Medium
Software Engineer

Convert stack samples to trace events

Question Implement convertToTrace(samples) that, given a chronologically ordered vector of stack samples (each sample contains a timestamp and a call-...

Coding & Algorithms
181
2
671 people solved
Jul 29, 2025

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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