Anthropic

AI safety and research company behind Claude, focusing on constitutional AI.

5 Rounds ~20 Days Very Hard

Start Mock Interview

The Interview Loop

Recruiter Screen (30 min)

Standard fit check, behavioral questions, and resume overview.

Technical Loop (3-4 Rounds)

Deep dive into domain knowledge, coding, and system design.

Interview Question Bank

All Roles Backend Engineer 35 Cloud Engineer 50 Data Engineer 85 Data Scientist 84 DevOps Engineer 35 Frontend Engineer 35 Full Stack Engineer 35 Machine Learning Engineer 85 Product Manager 85 Software Engineer 85

All Topics System Design 9 Frontend 6 Algorithms 6 Leadership 2 Backend 2 Culture Fit 2 Networking 2 Problem Solving 1

Full Stack Engineer • Behavioral • medium

Tell me about a time you had to balance shipping a feature quickly versus ensuring it met strict safety, security, or quality standards. How did you navigate the trade-off?

#Safety #Prioritization #Decision Making

Practice

Full Stack Engineer • Behavioral • medium

Anthropic places a heavy emphasis on Constitutional AI and alignment. How do you approach building user interfaces or product features where the underlying model's behavior might be non-deterministic or unpredictable?

#UX Design #Ambiguity #AI Integration

Practice

Full Stack Engineer • Behavioral • medium

Describe a situation where you disagreed with a researcher, data scientist, or product manager on how to implement a feature. How did you resolve the disagreement?

#Conflict Resolution #Communication #Cross-functional

Practice

Full Stack Engineer • Behavioral • medium

Tell me about a time you had to dive deep into a completely unfamiliar part of the stack or a new technology to debug a critical production issue.

#Debugging #Adaptability #Learning

Practice

Full Stack Engineer • Behavioral • hard

Give an example of a time you identified a fundamental flaw in a system's architecture. How did you advocate for fixing it, and what was the outcome?

#Architecture #Advocacy #Impact

Practice

Full Stack Engineer • Behavioral • medium

How do you prioritize your engineering tasks when working in an environment where sudden AI research breakthroughs can drastically change product roadmaps overnight?

#Adaptability #Agile #Prioritization

Practice

Full Stack Engineer • Behavioral • easy

Tell me about a time you mentored a junior engineer or helped a non-technical team member understand a highly complex technical concept.

#Mentorship #Communication #Empathy

Practice

Full Stack Engineer • Behavioral • medium

What is your approach to writing automated tests for non-deterministic systems, such as user interfaces that depend on generative LLM outputs?

#Testing #Mocks #Non-determinism

Practice

Full Stack Engineer • Coding • medium

Implement a React component that consumes a Server-Sent Events (SSE) endpoint to display a streaming text response from an LLM. It must gracefully handle connection drops and auto-scroll to the bottom as new text arrives.

#React #SSE #Streaming #DOM Manipulation

Practice

Full Stack Engineer • Coding • hard

Write a rate limiter middleware in Node.js/TypeScript using Redis. Unlike standard rate limiters, this must limit based on the number of 'tokens' consumed, which is only known after the API request completes.

#Node.js #Redis #Concurrency #API Design

Practice

Full Stack Engineer • Coding • hard

Implement a Markdown parser function in TypeScript that can render code blocks with syntax highlighting while the text is still streaming in chunk by chunk.

#Parsing #TypeScript #Streaming #State Machines

Practice

Full Stack Engineer • Coding • medium

Given a massive log file of API requests, write a Python script to find the top 5 users who consumed the most tokens in any sliding 1-hour window.

#Python #Sliding Window #Data Processing

Practice

Full Stack Engineer • Coding • medium

Build a custom React hook `useChat` that manages message state, handles loading states, and provides a function to abort an ongoing LLM generation using AbortController.

#React Hooks #State Management #Fetch API #AbortController

Practice

Full Stack Engineer • Coding • medium

Implement an LRU (Least Recently Used) cache with a Time-To-Live (TTL) feature to temporarily store frequent, identical prompt responses and reduce inference load.

#Data Structures #Caching #Hash Maps #Linked Lists

Practice

Full Stack Engineer • Coding • medium

Write a function to merge overlapping text highlights. Given an array of objects representing start and end indices of safety flags in a text, return a merged array of non-overlapping intervals.

#Intervals #Sorting #Arrays

Practice

Full Stack Engineer • Coding • medium

Implement a debounce function that delays invoking a function until after `wait` milliseconds, but also guarantees execution at least once every `maxWait` milliseconds (useful for auto-saving chat drafts).

#JavaScript #Timers #Closures

Practice

Full Stack Engineer • Coding • hard

Write an algorithm to efficiently diff two versions of a large text document and highlight the insertions and deletions. This is used to show users how their prompt edits changed the context.

#Dynamic Programming #Strings #Diff Algorithms

Practice

Full Stack Engineer • Coding • hard

Implement a custom JSON parser that can gracefully handle and 'fix' truncated JSON strings. This is common when an LLM output stops mid-generation due to max token limits.

#Parsing #Strings #Error Handling #AST

Practice

Full Stack Engineer • Coding • medium

Write a function to recursively traverse a DOM tree and extract its text content while maintaining semantic spacing (e.g., adding line breaks for block elements like <p> or <div>).

#DOM #Recursion #Trees

Practice

Full Stack Engineer • Coding • medium

Implement a concurrent task scheduler in Node.js that takes an array of asynchronous tasks and limits the number of active API requests to an external service to exactly `N`.

#Concurrency #Promises #Node.js

Practice

Full Stack Engineer • System Design • hard

Design the backend architecture for Claude's chat interface. Focus specifically on how you would handle low-latency streaming of tokens to the client while simultaneously persisting the conversation history to a database.

#Architecture #Streaming #Database Design #Concurrency

Practice

Full Stack Engineer • System Design • hard

Design a telemetry and logging system for LLM outputs that allows researchers to query for safety violations or model hallucinations, without compromising user privacy or storing PII.

#Privacy #Data Pipelines #Security #Analytics

Practice

Full Stack Engineer • System Design • hard

Design a system to handle prompt injection detection. This system must evaluate user input before it reaches the core LLM inference engine, adding no more than 50ms of latency.

#Security #Low Latency #Microservices #Machine Learning

Practice

Full Stack Engineer • System Design • medium

Design an internal annotation tool for researchers to rate and compare model responses (RLHF). It needs to handle concurrent edits, offline support, and high data integrity.

#Internal Tools #Offline First #Concurrency #Data Integrity

Practice

Full Stack Engineer • System Design • hard

Design a scalable document ingestion pipeline that extracts text from user-uploaded PDFs, chunks it, generates embeddings, and stores it in a vector database for RAG.

#Pipelines #Vector Databases #Asynchronous Processing #RAG

Practice

Full Stack Engineer • System Design • hard

Design a usage billing system for an LLM API that charges based on both input and output tokens. It must handle millions of requests per minute and ensure customers are never overcharged.

#Billing #Distributed Systems #Event Sourcing #Idempotency

Practice

Full Stack Engineer • System Design • hard

Design a distributed queue system to manage LLM inference requests. It must prioritize paid tier users over free tier users during high load, while preventing free tier starvation.

#Queueing Theory #Distributed Systems #Fairness #Load Balancing

Practice

Full Stack Engineer • System Design • hard

Design an A/B testing framework specifically for evaluating different versions of an LLM prompt or model weights in production, measuring both user engagement and safety metrics.

#Experimentation #Analytics #Routing #Data Engineering

Practice

Full Stack Engineer • System Design • hard

Design a system for users to upload, manage, and query against their own custom datasets (up to 10GB per user) within a chat interface. How do you ensure isolation and fast retrieval?

#Multi-tenancy #Storage #Search #Security

Practice

Full Stack Engineer • Technical • medium

How would you design a database schema to efficiently store and retrieve multi-turn chat conversations that support branching (e.g., when a user edits a previous prompt and generates a new response path)?

#SQL #Data Modeling #Trees/Graphs

Practice

Full Stack Engineer • Technical • medium

Explain how you would handle WebSocket connection drops and state reconciliation in a real-time collaborative prompt-engineering application.

#WebSockets #State Management #CRDTs/OT

Practice

Full Stack Engineer • Technical • hard

How do you optimize a frontend application to handle rendering massive DOMs, such as displaying a 100,000-word context window in a chat UI without freezing the browser?

#Performance #Virtualization #DOM #Web Workers

Practice

Full Stack Engineer • Technical • easy

Discuss the trade-offs between using Server-Sent Events (SSE), WebSockets, and long-polling for streaming LLM responses to a web client.

#Protocols #Streaming #Web Architecture

Practice

Full Stack Engineer • Technical • medium

How would you secure an internal dashboard that interacts with sensitive model training data and allows researchers to trigger fine-tuning jobs?

#Authentication #Authorization #Audit Logging #Network Security

Practice

Full Stack Engineer • Technical • medium

Explain how you would implement optimistic UI updates for a chat application where the server validation (e.g., a safety filter) might occasionally fail and reject the message.

#UX #State Management #Error Handling

Practice

Difficulty Radar

Based on recent AI-sourced data.

Meet Your Interviewers

The "Standard" Interviewer

Senior Engineer

Focuses on core competencies, system constraints, and clear communication.

Simulate

Unwritten Rules

Think Out Loud

Always explain your thought process before writing code or drawing architecture.

Practice Now

Anthropic

The Interview Loop

Recruiter Screen (30 min)

Technical Loop (3-4 Rounds)

Interview Question Bank

Tell me about a time you had to balance shipping a feature quickly versus ensuring it met strict safety, security, or quality standards. How did you navigate the trade-off?

Anthropic places a heavy emphasis on Constitutional AI and alignment. How do you approach building user interfaces or product features where the underlying model's behavior might be non-deterministic or unpredictable?

Describe a situation where you disagreed with a researcher, data scientist, or product manager on how to implement a feature. How did you resolve the disagreement?

Tell me about a time you had to dive deep into a completely unfamiliar part of the stack or a new technology to debug a critical production issue.

Give an example of a time you identified a fundamental flaw in a system's architecture. How did you advocate for fixing it, and what was the outcome?

How do you prioritize your engineering tasks when working in an environment where sudden AI research breakthroughs can drastically change product roadmaps overnight?

Tell me about a time you mentored a junior engineer or helped a non-technical team member understand a highly complex technical concept.

What is your approach to writing automated tests for non-deterministic systems, such as user interfaces that depend on generative LLM outputs?

Implement a React component that consumes a Server-Sent Events (SSE) endpoint to display a streaming text response from an LLM. It must gracefully handle connection drops and auto-scroll to the bottom as new text arrives.

Write a rate limiter middleware in Node.js/TypeScript using Redis. Unlike standard rate limiters, this must limit based on the number of 'tokens' consumed, which is only known *after* the API request completes.

Implement a Markdown parser function in TypeScript that can render code blocks with syntax highlighting *while* the text is still streaming in chunk by chunk.

Given a massive log file of API requests, write a Python script to find the top 5 users who consumed the most tokens in any sliding 1-hour window.

Build a custom React hook `useChat` that manages message state, handles loading states, and provides a function to abort an ongoing LLM generation using AbortController.

Implement an LRU (Least Recently Used) cache with a Time-To-Live (TTL) feature to temporarily store frequent, identical prompt responses and reduce inference load.

Write a function to merge overlapping text highlights. Given an array of objects representing start and end indices of safety flags in a text, return a merged array of non-overlapping intervals.

Implement a debounce function that delays invoking a function until after `wait` milliseconds, but also guarantees execution at least once every `maxWait` milliseconds (useful for auto-saving chat drafts).

Write an algorithm to efficiently diff two versions of a large text document and highlight the insertions and deletions. This is used to show users how their prompt edits changed the context.

Implement a custom JSON parser that can gracefully handle and 'fix' truncated JSON strings. This is common when an LLM output stops mid-generation due to max token limits.

Write a function to recursively traverse a DOM tree and extract its text content while maintaining semantic spacing (e.g., adding line breaks for block elements like <p> or <div>).

Implement a concurrent task scheduler in Node.js that takes an array of asynchronous tasks and limits the number of active API requests to an external service to exactly `N`.

Design the backend architecture for Claude's chat interface. Focus specifically on how you would handle low-latency streaming of tokens to the client while simultaneously persisting the conversation history to a database.

Design a telemetry and logging system for LLM outputs that allows researchers to query for safety violations or model hallucinations, without compromising user privacy or storing PII.

Design a system to handle prompt injection detection. This system must evaluate user input before it reaches the core LLM inference engine, adding no more than 50ms of latency.

Design an internal annotation tool for researchers to rate and compare model responses (RLHF). It needs to handle concurrent edits, offline support, and high data integrity.

Design a scalable document ingestion pipeline that extracts text from user-uploaded PDFs, chunks it, generates embeddings, and stores it in a vector database for RAG.

Design a usage billing system for an LLM API that charges based on both input and output tokens. It must handle millions of requests per minute and ensure customers are never overcharged.

Design a distributed queue system to manage LLM inference requests. It must prioritize paid tier users over free tier users during high load, while preventing free tier starvation.

Design an A/B testing framework specifically for evaluating different versions of an LLM prompt or model weights in production, measuring both user engagement and safety metrics.

Design a system for users to upload, manage, and query against their own custom datasets (up to 10GB per user) within a chat interface. How do you ensure isolation and fast retrieval?

How would you design a database schema to efficiently store and retrieve multi-turn chat conversations that support branching (e.g., when a user edits a previous prompt and generates a new response path)?

Explain how you would handle WebSocket connection drops and state reconciliation in a real-time collaborative prompt-engineering application.

How do you optimize a frontend application to handle rendering massive DOMs, such as displaying a 100,000-word context window in a chat UI without freezing the browser?

Discuss the trade-offs between using Server-Sent Events (SSE), WebSockets, and long-polling for streaming LLM responses to a web client.

How would you secure an internal dashboard that interacts with sensitive model training data and allows researchers to trigger fine-tuning jobs?

Explain how you would implement optimistic UI updates for a chat application where the server validation (e.g., a safety filter) might occasionally fail and reject the message.

Difficulty Radar

Meet Your Interviewers

The "Standard" Interviewer

Unwritten Rules

Write a rate limiter middleware in Node.js/TypeScript using Redis. Unlike standard rate limiters, this must limit based on the number of 'tokens' consumed, which is only known after the API request completes.

Implement a Markdown parser function in TypeScript that can render code blocks with syntax highlighting while the text is still streaming in chunk by chunk.