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Anthropic's Interview Process (2026)

Blog / Anthropic's Interview Process (2026)

The Anthropic software engineer interview process is generally more involved than a typical big tech pipeline, and most candidates report going through five to six stages before reaching an offer. Here is what the process typically looks like:

Recruiter Screen: A 30-minute conversation covering your background, your motivation for joining Anthropic specifically, and your general interest in AI safety. Recruiters often flag early that the behavioral and values rounds carry as much weight as the technical ones.
Online Assessment: A 90-minute CodeSignal assessment, typically featuring two multi-part problems. Expect to build a small system from scratch rather than solve isolated algorithmic puzzles, and aim for production-quality code throughout.
Hiring Manager Screen: A 45 to 60 minute technical conversation focused on your engineering judgment rather than live coding. You may be asked to analyze a codebase, identify bottlenecks, or discuss how you would scale a system significantly.
Virtual Onsite - Loop 1: Two to three rounds covering coding and system design, often held on the first day of the onsite. Passing this loop is generally required before Loop 2 is scheduled.
Virtual Onsite - Loop 2: Two to three rounds focused on culture fit, values alignment, and a formal project deep dive where you present and defend a past project under close technical scrutiny.
Reference Checks and Team Matching: A final verification stage before the offer. Anthropic's reference checks are reported to be thorough, and team matching can sometimes extend the overall timeline.

To prepare effectively, organize your study plan around the core question types that show up across Anthropic's technical rounds:

Data Structures & Algorithms (DSA): Focused on practical coding problems requiring production-quality, concurrency-aware implementations.
Low-Level Design (LLD): Building real systems from scratch, often with thread-safety and incremental complexity requirements.
System Design (HLD): Highly domain-specific design problems centered on LLM infrastructure, GPU scheduling, and large-scale distributed systems.
Behavioral and Values: A uniquely rigorous round probing ethical judgment, safety alignment, and how you handle moral conflicts at work.
Frontend Engineering: Real-time UI challenges tied to AI product workflows, including streaming interfaces and cross-platform chat architectures.
SQL: Advanced query writing and schema design problems, often at scale or with complex analytical requirements.

1. Data Structures & Algorithms (DSA)Anthropic's coding rounds are less about pure algorithmic theory and more about writing code you would actually ship. Problems tend to involve building functional systems incrementally, so clean structure and error handling matter as much as correctness.Concurrency is the most consistently reported theme across candidate feedback. Expect to implement something like an LRU Cache that must be thread-safe, or tackle profiler trace problems like Stack Trace to Execution Trace that require careful handling of call stack state.Other reported problems include tokenization engines that deal with text streaming and buffering, and duplicate file finders that test your comfort with hashing and file system traversal. Brush up on stacks, heaps, and graphs, since these structures appear frequently in the system-building problems Anthropic favors.For foundational preparation, working through our top 100 DSA questions will cover the core patterns you need.Prioritize problems that involve concurrency, iterative refinement, and real-world constraints over pure puzzle-style questions.

2. Low-Level Design (LLD)The online assessment and coding rounds often ask you to build a working system from scratch inside 90 minutes, and the bar is explicitly production quality. Common examples include an in-memory key-value store with concurrent read/write support and a task management system with dependency resolution.The Multi-threaded Web Crawler is one of the most frequently cited problems, often introduced first as a synchronous implementation and then extended to handle async or multithreaded execution. This incremental format is common, so practice building systems in layers rather than trying to solve everything upfront.Anthropics interviewers specifically watch for how you handle failure modes: what happens when a network call hangs, when a thread deadlocks, or when input is malformed. Get comfortable with our Low-Level Design practice examples and make concurrency primitives like locks and async patterns second nature before your onsite.

3. System Design (HLD)Anthropics system design round is not a generic whiteboard exercise. You are expected to design infrastructure that is specific to how LLMs actually work in production, so a surface-level understanding of distributed systems is not enough.Frequently reported prompts include designing an inference batching system that queues requests for a single GPU while keeping latency acceptable for users, and a distributed search system capable of handling a billion documents and millions of queries per second. GPU scheduling and request routing across clusters also come up regularly.Even if you are not coming from an ML background, you should understand the basics of how inference serving works, including concepts like KV cache management, batching strategies, and GPU memory constraints. Review our High-Level Design questions and use the System Design Whiteboard to practice drawing out architectures under time pressure. Grounding yourself in system design core concepts and caching fundamentals will also help significantly.

4. Behavioral and ValuesAnthropics values round is consistently described by candidates as the most intense and least expected part of the process. It goes well beyond standard behavioral questions and directly probes your ethical judgment and relationship with AI safety.Expect questions like: tell me about a time you did something that conflicted with your values, or how would you handle being assigned to a project you believe is unsafe. Interviewers are assessing whether you have genuinely internalized the tension between moving fast and being responsible, not just whether you can say the right things.Candidates who perform well in this round have typically read Anthropics Responsible Scaling Policy and can reference it naturally. Familiarity with their published work on Constitutional AI also signals genuine engagement rather than surface-level interest. Use the Behavioral Interview Course to structure your answers and the Behavioral Playbook to prepare strong, specific examples from your own experience.The project deep dive in Loop 2 is a separate but related challenge. You will present a past project for around 20 minutes and then face 40 minutes of pointed technical questions about your decisions and trade-offs. Come prepared to defend every architectural choice as if you were in a design review with a skeptical senior engineer.

5. Frontend EngineeringFrontend questions at Anthropic are tightly coupled to the products they actually build, so expect challenges that reflect real AI product workflows rather than generic UI exercises. Reported problems include building a streaming response component that renders model output in real time and implementing a Markdown and LaTeX renderer for chat interfaces.Cross-platform desktop chat architecture has also appeared, reflecting Anthropics investment in shipping Claude across multiple environments. These problems test whether you can think through performance, state management, and rendering edge cases at the same time.If you are applying to a frontend-oriented SWE role, make sure you are comfortable with streaming APIs, incremental rendering, and the quirks of rendering structured content like code blocks and mathematical notation inside a chat UI.

6. SQLSQL questions at Anthropic tend toward the analytical and schema design end of the spectrum rather than basic query writing. Reported problems include random row sampling from large tables, rolling averages using window functions, and temporal schema design for tracking historical records like address history.These problems often involve reasoning about performance at scale, so understanding query optimization and index behavior matters. Review SQL theory to make sure you are comfortable with window functions, CTEs, and schema normalization before your interview.

ConclusionAnthropics process rewards engineers who write careful, well-reasoned code and who can articulate their values around AI safety with real conviction, not just rehearsed answers. Start your preparation with the technical foundations, build up your concurrency skills, and take the behavioral round as seriously as any coding problem. Follow the Anthropic Interview Roadmap for a structured path through every stage of the process.