Anthropic Software Engineer Interview Guide

Anthropic's software engineering interview combines practical coding with conversations about how you'd build a product and work with the people around you. Coding takes up a substantial part of the process, but system design and the company's Culture interview deserve preparation time too.

Below, we explain what happens at each stage and where to focus your preparation, with recent interview questions reported by Anthropic SWE candidates. We keep that collection up to date as candidates share their experiences. Your recruiter can explain what to prepare for each round, since the details vary by team and role.

The early stages include a recruiter conversation, an online coding assessment when required, and a live technical screen. The assessment can arrive before the recruiter call, so look at the invitation before assuming your first step is a conversation. Once you pass the screen, you'll move into the virtual onsite, which brings together coding, system design, and behavioral and Culture interviews.

Candidates commonly describe two coding interviews, system design, and behavioral evaluation. The hiring-manager conversation focuses on your experience and collaboration, while the separate Culture interview explores your views on Anthropic's mission and AI safety. Leave preparation time for both.

Anthropic's careers guidance says interviews take place over Google Meet, with CodeSignal and Google Colab used for technical work. You'll write and run code in these browser-based environments, including test cases. Practice there beforehand so you're comfortable working without your usual IDE setup. Python is common in candidate reports, though language requirements vary by role. Some candidates have used Replit for the live screen.

Your first conversation with recruiting is a chance to talk about your background and understand what the team is hiring you to do. Candidates describe short calls, often around 15 to 30 minutes, covering their experience, interest in Anthropic, and motivation for working on AI. Expect a more specific question than whether you like using Claude. The recruiter wants to understand why this company and this role interest you.

Before the call, read about the team and think about how its work connects to yours. You don't need to have trained a language model to explain why you'd enjoy building developer tools, a customer-facing product, or reliable infrastructure.

Use your questions to understand the job itself. What would you work on in your first few months, who would you work with, and why is the team hiring? Those answers can help you decide whether the role fits what you want to do next.

When an online assessment is part of your process, expect a timed coding project. Candidates commonly report a 90-minute CodeSignal assessment with requirements that build on one another. An in-memory database is a representative example. You implement an initial set of operations, then extend the same code as more behavior is introduced.

CodeSignal's Industry Coding Assessment uses one project with four progressive levels. Anthropic candidates also report other versions, including a six-part assessment, so use your invitation for the exact format. This is a timed online assessment, not a take-home project you spend several evenings building.

The assessment supplies tests, and candidates describe needing to pass a stage's tests before the next stage unlocks. Practice extending code you've already written while keeping its earlier behavior working. LeetCode's Design category is useful preparation. After completing a problem, add a requirement and work through the change under a time limit.

Candidates have reported advancing with less than a perfect score and being rejected after a perfect score. Passing the tests matters, but the score alone doesn't determine whether you'll move forward.

Give yourself practice making changes under a deadline, as well as solving a problem from scratch.

For the assessment and live interviews, the default is no AI assistance. That includes Claude, ChatGPT, code-generation tools, and AI autocomplete. Anthropic's candidate guidance explicitly allows exceptions when the assessment instructions say so, and encourages using AI to prepare beforehand.

Even if you use AI to explain an unfamiliar concept during preparation, do timed practice without it. You need to be comfortable writing and debugging the implementation yourself on interview day.

The live screen focuses on practical implementation rather than a short LeetCode-style algorithm question. Expect to build or modify a component with several requirements. Reported examples include processing stack traces, transforming image data, working with JSON, and finding duplicate files. In an image-transformation task, for example, you could be asked to apply transformations described in JSON to an image. The work is in getting the required behavior implemented correctly within the session.

You'll usually work with an engineer for around 50 to 60 minutes in a shared coding environment. The interviewer can clarify the prompt and introduce follow-ups as you go. Candidate discussions of the live screen distinguish it from the longer online assessment and describe substantial implementation work in both.

Concurrency deserves extra attention if you're interviewing for infrastructure. Candidates report building a crawler or scraper and then making it run concurrently. LeetCode 1242, Web Crawler Multithreaded, is relevant practice for that format. Know how to use your language's worker pools, queues, and synchronization tools, and how to recognize when two workers could act on the same shared state.

Expect another practical implementation task in the onsite. The screen gives you a useful sense of how much code you'll need to produce, but the next question can use a different domain. Keep practicing complete, runnable implementations across different tasks. If the next round has a special focus, such as concurrency, your recruiter should give you preparation guidance for it.

After coding, the design interview gives you room to discuss a larger system without implementing every part of it. You'll work through what the product needs to do, how its data is represented, and how requests move through the system. Reported prompts include chat services, distributing large model files, and batching model runs.

In your preparation, work on explaining a complete design before getting absorbed in one difficult component. You should be able to describe the APIs and data model, then explain how the design handles the stated workload and a meaningful failure case. The interviewer will have questions about your choices, and the conversation should leave room to develop them.

Product-focused interviews can spend substantial time on user flows and data models. Infrastructure interviews can go further into throughput, scheduling, and recovery. Ask what kind of design session your team uses so you can choose practice problems with similar concerns.

Our system-design delivery guide gives you a structure for the discussion. WhatsApp is useful practice for messaging, while Design ChatGPT explores the product and backend considerations of a conversational AI service. Practice explaining why each design choice fits the requirements.

The hiring-manager conversation turns to work you've already done. Expect questions about a project, a disagreement, a mistake, or a decision you made with incomplete information. The manager can ask technical follow-ups, so choose examples where you remember what happened beyond the final outcome.

For a Software Engineer role, your examples don't need to involve setting strategy for an entire organization. A feature you owned, a difficult integration, or a production problem you helped resolve can give you plenty to discuss. Explain your responsibility, what you decided, and how you worked with the people involved. Be clear about which parts belonged to other engineers.

Our behavioral interview course explains how to prepare for these conversations. The Story Builder can help you choose and organize examples without turning them into a script.

Culture is a distinct part of Anthropic's process. The conversation asks how you think about AI's effects on society, the company's mission, and decisions where reasonable people disagree. It also draws on how you work with others and whether you're willing to change your mind.

One candidate on Blind described being asked whether they'd accept Anthropic's stock falling to zero in pursuit of its mission. Questions like that make the tradeoff personal. Spend time thinking about how your financial interests, responsibilities to users, and views on safety fit together.

Prepare to discuss a moral dilemma from your own work, including what you chose and who was affected.

Read the values and preparation guidance on Anthropic's careers page, then spend time forming your own views. What do you find promising about AI, what concerns you, and what would make you reconsider a belief? You should be able to discuss a tradeoff without assuming every answer has to end with agreement with the interviewer.

An example of changing your mind or taking criticism seriously is useful because the interviewer can ask what changed your thinking.

After the interviews, recruiting coordinates the remaining steps. Successful candidates in our community describe reference checks before the final hiring decision, sometimes alongside team matching. Think ahead about who can speak directly to the work and collaboration you discussed in the loop, and ask before sharing their contact details.

The wait can stretch for several weeks, including periods without an update after references. Candidates have also reported rejection at that stage. Keep other interview processes moving until you have an offer, and tell recruiting about any competing deadline. A former manager and a close collaborator are useful references to have ready.