Anthropic Interview Questions
The coding and system-design questions asked in Anthropic interviews - with worked solutions, in-browser code execution, and active-recall review. 8 questions.
How Anthropic interviews software engineers
Anthropic's software-engineer loop is a recruiter screen, a system-design technical screen, then a virtual on-site of five back-to-back rounds. It is fully remote: coding happens in a shared Python environment, and design is on a whiteboard tool you bring yourself (Excalidraw, draw.io - test screen-share before the call). First contact to offer averages about three weeks.
The design rounds reuse a small, stable bank of AI-infrastructure questions - batched inference and large model-file distribution are the most reported. They reward stripping the AI framing down to the underlying systems problem, naming the crux early, doing back-of-envelope math, and covering failure modes and observability before being asked. The final culture round is pass/fail and centers on AI safety.
The interview loop
- 1Recruiter screen~30 min by phone. Role fit, comp alignment, timeline, and a first pass on why Anthropic specifically (the company and mission, not just 'AI is exciting').
- 2System-design screen55 min over video on a whiteboard tool you bring. The first high-signal filter, often a known prompt like batched inference or large model-file distribution. Start simple and scale under pressure; overengineering is the most-cited rejection reason.
- 3Virtual on-siteFive back-to-back rounds of ~55 min each:
- System design #2 - A second design prompt, often a variant of the screen - async inference, model serving, or content-addressed dedup.
- Coding / code productivity - Pick your language. A multi-level implementation problem (the in-memory key-value store is the most reported), each level unlocking the next. Clean, tested code over raw speed.
- Technical project discussion - ~20 min presenting a past project, then deep-dive probes on your decisions, trade-offs, failures, real scope, and numbers.
- Hiring-manager deep dive - STAR-style: ownership, influencing without authority, competing priorities, and what staff-level scope means to you.
- Culture & values - Pass/fail and Socratic, on AI safety: why Anthropic, the biggest risks, speed vs safety, and how your work connects to the mission.
What they look for
- Stripping the AI framing to the underlying systems problem, then naming the crux fast
- Driving the design yourself - scope, decide, draw; the interviewer's silence is intentional
- Back-of-envelope math, proactive failure modes, and observability on every component
- Clean, tested Python over raw speed in the coding round
- Genuine, specific engagement with AI safety - not generic enthusiasm
How to prepare
- Prep the two near-certain design prompts cold: batched inference and large model-file distribution. Start dead simple - overengineering is the top cited rejection reason.
- Pick one approach and defend it briefly; presenting a menu of three options reads as indecisive.
- Raise failure modes and what you would instrument before the interviewer asks.
- Rehearse a 20-minute project walkthrough with specific numbers and the parts you got wrong.
- For the culture round, read Anthropic's materials below and form real opinions - including something you would push back on.
Preparing for the AI-safety culture round
The culture round is pass/fail and deliberately Socratic - they are testing whether you have genuinely thought about AI risk, not whether you can recite Anthropic's positions. Anthropic sends reading beforehand (usually Core Views on AI Safety and the Responsible Scaling Policy, sometimes a research paper). Most candidates skim it; the ones who actually engage - form opinions, find something to push back on - stand out.
Be ready to connect your specific work to safety outcomes concretely. For infrastructure roles, the credible frame is that CI/CD is the trust layer between what you intend to ship and what actually runs: evaluation gates, artifact integrity, reproducible hermetic builds, and access control are how a safety policy becomes enforceable rather than aspirational.
Worth understanding at a conceptual level: Constitutional AI (training to a written set of principles via AI feedback), the Responsible Scaling Policy and its ASL capability thresholds that gate training and deployment, and mechanistic interpretability (reverse-engineering what happens inside the model - auditability at the model layer).
- Anthropic - Core Views on AI Safety - Why Anthropic exists and how it frames AI risk.
- Anthropic - Responsible Scaling Policy - ASL capability thresholds that gate training and deployment.
- Anthropic - Constitutional AI - The alignment technique behind Claude.
- Machines of Loving Grace - Dario Amodei - Dario's vision of a world with powerful AI done right - table stakes for 'why Anthropic'.
Related system-design questions
- Batched Inference Service for an LLM APIComing soon
- Claude Chat ServiceComing soon
Sources
- interviewing.io - Anthropic interview questions & process - First-hand process breakdown and sample questions.
- IGotAnOffer - Anthropic system design interview - Format and prompts for the AI-infrastructure design round.
- InterviewQuery - Anthropic software engineer guide - Stage-by-stage overview and prep.
- Candidate writeup - 2025 Anthropic SWE interview - Recent first-hand account of the full loop.
See every company's references on the interview-process sources page.
Practice questions
- AI Safety: A Primer for Engineering Interviews
Why frontier AI labs probe your safety thinking, the core concepts to know, and a curated reading list - for Anthropic's culture round and beyond.
Article12m1mo - Stack Samples → Trace Events
A sampling profiler captures call stacks at fixed intervals. Convert those snapshots into start/end span events. Three phases - basic diff, recursion, then a min-consecutive-count debounce.
Practicalhard60m2mo - Sampling Profiler → Trace Events (Sample/Event types)(premium)
A typed variant of the stack-samples problem - Sample and Event are dataclasses, comparisons are by value, and the contract is exposed via the imports the test file requires.
Practicalhard60m2mo - Find Duplicate Files(premium)
Group identical files by content. Stream-hash to keep memory bounded, then add size pre-filtering and an opt-in symlink/min-size policy.
Practicalmedium45m2mo
+4 - Find Duplicate Files - Extended(premium)
Same starting point as the basic duplicate-finder, then six layers: streaming hash, size + symlink filters, recursive directory walking, two-pass hashing, per-file error handling, and a persistent hash cache.
Practicalhard90m2mo - In-Memory Database with SQL-like Operations(premium)
A single-table in-memory database supporting INSERT and SELECT with WHERE (AND-only equality) and multi-column ORDER BY. The stable-sort trick is the trade tool.
Practicalmedium40m2mo
- Web Crawler(premium)
Build a web crawler on top of an injected get_links helper - breadth-first crawl with deduplication, then same-domain and fragment handling, then a concurrent version with a thread pool and a shared visited set, and finally depth and global rate limits. A concurrency problem disguised as a graph traversal.
Practicalhard50m1mo - Image Processing Pipeline(premium)
Apply ordered image operations (resize, rotate, crop, blur, grayscale) with Pillow - one image, then every image-by-pipeline combination, then a thread pool, then partial-failure isolation, then directory discovery. A throughput and fault-tolerance problem dressed as image manipulation.
Practicalmedium45m1mo - Image Pipeline - Recursive Batch(premium)
The directory-scale follow-up to Image Processing Pipeline. Walk a tree of images (Large/ and Small/ subfolders), load transform lists from a folder of JSON files, and apply every transform to every image in parallel - preserving the source subdirectory layout in the output and isolating per-combination failures. The fan-out the interview actually scales to millions of combinations.
Practicalmedium45m1mo