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
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Anthropic
Medium
Software Engineer

Design an in-memory banking service

Design an in-memory banking service supporting timestamped operations and edge-case semantics. Implement: ( 1) create_account(id, t): Create a new acc...

Coding & Algorithms
105
1
922 people solved
Sep 6, 2025
Anthropic logo
Anthropic
Hard
Software Engineer

Design a desktop AI chat frontend

Design a Frontend Architecture for a Cross-Platform Desktop Conversational AI App Context You are designing the frontend architecture for a cross-plat...

System Design
41
0
825 people solved
Sep 6, 2025
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Anthropic
Hard
Software Engineer

Explain CPU-Bound vs I/O-Bound Work

Define CPU-bound and I/O-bound workloads. Then compare how multithreading, asynchronous I/O, and multiprocessing behave for each type of workload, esp...

Software Engineering Fundamentals
23
0
242 people solved
Jan 6, 2026
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Anthropic
Hard
Software Engineer

Calculate Streaming Token Usage Costs

Calculate Streaming Token Usage Costs Implement a usage calculator for language-model API requests. Input and output tokens have different prices, and...

Coding & Algorithms
1
0
14 people solved
Jul 8, 2026
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Anthropic
Hard
Software Engineer

Simulate Threshold Infection Efficiently

Simulate Threshold Infection Efficiently You are given an m x n grid, a set of initially infected cells, and an integer threshold k. Time advances in ...

Coding & Algorithms
2
0
12 people solved
Jul 8, 2026
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Anthropic
Medium
Machine Learning Engineer Locked

Convert Samples into Event Intervals

This question evaluates understanding of array and sequence processing, run-length encoding concepts, and interval representation for time-ordered tra...

Coding & Algorithms
15
0
94 people solved
Apr 7, 2026
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Anthropic
Hard
Software Engineer

Review an inference API design for scale

System Design Review: A Machine-Learning Inference API at Scale Background You are reviewing a teammate's design document for a production machine-lea...

ML System Design
22
0
343 people solved
Sep 6, 2025
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Anthropic
Hard
Software Engineer

Describe failure impact and resolve cross-functional conflict

You are in a behavioral interview. Answer the following prompts using a structured method (e.g., STAR or CARL). Provide specific details, metrics wher...

Behavioral & Leadership
14
0
238 people solved
Oct 9, 2025
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Anthropic
Hard
Software Engineer

Design a batch inference API

Design an Asynchronous (POST-and-Poll) Inference Service API Design an asynchronous inference service for serving model predictions. A client submits ...

ML System Design
67
0
1017 people solved
Sep 6, 2025
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Anthropic
Hard
Software Engineer

Design a low-latency ML inference API

System Design: Low-Latency ML Inference API (Real-Time) Context You are designing an in-region, synchronous ML inference API that sits on the critical...

ML System Design
62
0
785 people solved
Sep 6, 2025
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Anthropic
Medium
Software Engineer

Why Anthropic and its values?

This interview evaluates culture fit, value alignment, and critical thinking for Anthropic. Prepare one coherent discussion that addresses all of the ...

Behavioral & Leadership
30
0
221 people solved
Oct 19, 2025
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Anthropic
Medium
Machine Learning Engineer

Optimize MapReduce performance

Optimize MapReduce for Parallel Efficiency and Network Utilization You are designing a large-scale batch processing job (e.g., feature extraction, log...

System Design
28
0
482 people solved
Aug 4, 2025
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Anthropic
Hard
Software Engineer Locked

Estimate VRAM and compare model parallelism

This question evaluates understanding of GPU memory budgeting for large matrix multiplications and the comparative trade-offs between pipeline and ten...

ML System Design
34
0
276 people solved
Nov 19, 2025
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Anthropic
Hard
Software Engineer

Design a scalable network I/O service

System Design: High-Volume Network I/O Backend (Files and Streaming) Context Design a backend service that supports millions of users uploading and do...

System Design
30
0
348 people solved
Sep 6, 2025
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Anthropic
Hard
Software Engineer

Design production-ready dedup service

System Design: Production-Ready File Deduplication Service Context Design a multi-tenant cloud service that stores files and achieves space savings vi...

System Design
24
0
438 people solved
Sep 6, 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

Explain projects and handle AI-safety conflicts

Behavioral / Hiring Manager round 1. Walk through 1–2 key projects from your resume. - What was the goal and why did it matter? - What was your ...

Behavioral & Leadership
32
0
312 people solved
Nov 19, 2025
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Anthropic
Hard
Software Engineer

Walk through a recent technical project

Project Deep-Dive (Onsite Behavioral + Technical) Context: Choose a recent technical project (ideally within the last 12–18 months) where you led or h...

Behavioral & Leadership
19
0
265 people solved
Sep 6, 2025
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Anthropic
Medium
Machine Learning Engineer

Describe communication to resolve ambiguity

Describe communication to resolve ambiguity Behavioral: Proactive Communication to Improve Outcomes Context: In a technical screen for a Machine Learn...

Behavioral & Leadership
14
0
157 people solved
Aug 1, 2025
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Anthropic
Hard
Software Engineer

Implement a thread-safe producer–consumer buffer

Implement a thread-safe producer–consumer buffer Bounded Blocking Buffer with Shutdown and Timeouts You are asked to design and implement a thread-saf...

System Design
40
0
346 people solved
Jul 31, 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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