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 Locked

Design a distributed web crawler

This question evaluates the ability to design scalable, fault-tolerant distributed systems for web crawling, covering competencies such as URL dedupli...

System Design
83
0
580 people solved
Jan 6, 2026
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
Anthropic logo
Anthropic
Hard
Software Engineer

Design a scalable, reliable system

System Design: Global Photo/Video File Storage and Sharing ("CloudDrive") Context Design a scalable, highly reliable consumer service where users uplo...

System Design
33
0
329 people solved
Sep 6, 2025
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Anthropic
Hard
Software Engineer

Guide and override compiler optimizations

Guiding a Compiler for a VLIW-like Backend You are optimizing hot loops for a VLIW-like target (e.g., DSP/AI accelerator) where the compiler sometimes...

System Design
10
0
169 people solved
Sep 6, 2025
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Anthropic
Hard
Software Engineer

Schedule instructions on a VLIW pipeline

VLIW Instruction Scheduling and Software Pipelining You are given a 4-issue VLIW machine with these functional units and latencies: - 2 ALUs (ALU late...

System Design
31
0
218 people solved
Sep 6, 2025
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Anthropic
Medium
Machine Learning Engineer

Convert stack samples to execution trace

You are given sampling-profiler output: a list of Sample objects ordered by timestamp ascending. Each Sample has (t: float, stack: list[str]) where st...

Coding & Algorithms
52
0
855 people solved
Sep 6, 2025
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Anthropic
Hard
Software Engineer

Discuss career decisions and culture fit

Behavioral and Leadership Technical Screen — Software Engineer Context You’ll be assessed on how you make and communicate decisions, collaborate acros...

Behavioral & Leadership
31
0
212 people solved
Sep 6, 2025
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Anthropic
Medium
Software Engineer

Present project and answer behaviorals

Behavioral & Leadership Project Presentation (Software Engineer) You are interviewing onsite for a Software Engineer role. Prepare a slide-style narra...

Behavioral & Leadership
18
0
192 people solved
Sep 6, 2025
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Anthropic
Medium
Software Engineer

Parse and Reconstruct Stack Trace

Given a multi-line stack trace string from a single thread (e.g., each frame is in the form 'at Module::Function(file:line)'), design and implement a ...

Coding & Algorithms
44
0
663 people solved
Sep 6, 2025
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Anthropic
Medium
Machine Learning Engineer Locked

Convert State Stream to Events

This question evaluates a candidate's ability to process sequential categorical data by identifying and summarizing consecutive runs, reason about tim...

Coding & Algorithms
47
0
453 people solved
Apr 6, 2026
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Anthropic
Hard
Software Engineer Locked

Implement Persistent Memoization LRU Cache

This question evaluates knowledge of memoization, LRU cache semantics, deterministic cache-key construction from positional and keyword arguments, JSO...

Coding & Algorithms
8
0
67 people solved
May 23, 2026
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Anthropic
Hard
Software Engineer

Identify and mitigate deduplication program risks

System Design: Robust File Deduplication in the Real World Context You are designing a file deduplication tool that scans large directory trees on one...

System Design
27
0
189 people solved
Aug 14, 2025
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Anthropic
Hard
Machine Learning EngineerSenior+ Locked

Implement a Simplified DNS Resolver

This question evaluates understanding of DNS semantics (name normalization, A and CNAME records, alias chains and cycle detection), robust error handl...

Coding & Algorithms
23
0
202 people solved
May 16, 2026
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Anthropic
Medium
Machine Learning Engineer Locked

Implement an LRU cache

This question evaluates competency in designing efficient key-value caching mechanisms, enforcing capacity constraints and eviction policies while mai...

Coding & Algorithms
22
0
349 people solved
Feb 27, 2026
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Anthropic
Medium
Software Engineer

Explain career goals and cultural alignment

Explain career goals and cultural alignment Behavioral: Long‑Term Goals and Mission Alignment (Software Engineer, Onsite) Prompt You are interviewing ...

Behavioral & Leadership
22
0
146 people solved
Jul 29, 2025
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Anthropic
Hard
Software Engineer

Design a prompt processing backend

Design a prompt processing backend System Design: Background Processing Backend for LLM Prompts Context Design a multi-tenant backend that processes l...

ML System Design
13
0
170 people solved
Jul 26, 2025
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Anthropic
Medium
Software Engineer

Simulate stack traces from logs

Given a list of log entries describing function calls, each formatted as "<id> <event> <timestamp>" where event ∈ {START, END} and timestamps are inte...

Coding & Algorithms
51
0
471 people solved
Sep 6, 2025
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Anthropic
Hard
Software Engineer

Design a profiling plan for kernels

Rigorous Profiling and Experimentation Plan for a Kernel Simulator You are given only a kernel simulator that reports cycle counts and microarchitectu...

Analytics & Experimentation
21
0
164 people solved
Sep 6, 2025
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Anthropic
Hard
Software Engineer Locked

Design a batched inference API

This question evaluates competency in designing scalable, low-latency ML inference systems with dynamic batching, covering system architecture, reques...

ML System Design
7
0
87 people solved
Feb 8, 2026
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Anthropic
Hard
Software Engineer

Group Duplicate Files by Content

Group Duplicate Files by Content Given a snapshot of files, return groups of paths whose complete contents are identical. The interview report preserv...

Coding & Algorithms
0
0
6 people solved
Apr 16, 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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