I recently finished the Anthropic SWE Virtual Onsite after completing OA1, OA2, and the Take-Home Project.
Anthropic's SWE interview process feels quite different from that of most traditional tech companies. It is not simply about solving LeetCode problems quickly. The interviews focus much more on whether you can design reliable systems in realistic engineering scenarios, reason about concurrency and distributed systems, and think seriously about AI safety.
The four rounds moved quickly, and interviewers frequently added new constraints after I proposed a solution. The key was being able to adapt the design while keeping the system reliable.
My four rounds were:
- R1: Coding — Rate Limiter
- R2: Coding — Lock-Free Queue
- R3: System Design — Web Crawler
- R4: Behavioral — AI Safety, Teamwork, and Why Anthropic
Each round lasted approximately 60 minutes.
Anthropic SWE Interview Process Overview
The overall process I went through was:
OA1 (CodeSignal) → OA2 → Take-Home Project → Four-Round VO → Offer
The VO can be completed in one day or split across two days depending on scheduling. Unlike a typical LeetCode-style interview, the focus is heavily on practical engineering, system behavior, trade-offs, and communication.
Round 1: Coding — Distributed Rate Limiter
The first round started directly with an engineering implementation problem: design a rate limiter.
The requirements included:
- Maximum 100 requests per user per minute
- Maximum 1,000 requests per organization per minute
- The service needs to work correctly in a distributed environment
I started with a Token Bucket implementation. Each bucket keeps track of the remaining tokens and the last refill timestamp. When a request arrives, we calculate how many tokens should have been replenished and then determine whether the request can proceed.
The interviewer quickly added another constraint:
If the rate limiter is deployed across multiple machines, how would you keep the counters consistent?
This required moving the shared state outside of the individual application instances. I suggested using Redis with atomic operations and TTL-based expiration.
The next follow-up was:
What happens if Redis becomes slow or temporarily unavailable?
Instead of simply retrying everything, I discussed a graceful degradation strategy. For example, we could maintain a short-lived local cache and tolerate a small amount of temporary counting inconsistency while monitoring Redis health and triggering alerts.
The interviewer also asked about the trade-offs between Token Bucket and Sliding Window approaches.
Token Bucket:
- Relatively simple to implement
- Handles bursts naturally
- Does not provide the same precision as a sliding-window approach for certain short time windows
Sliding Window:
- More precise control
- Can require more memory and computation
This round made one thing very clear:
Anthropic cares a lot about engineering trade-offs.
It is not enough to know how to implement something. You also need to explain why you chose that design and what you would sacrifice by choosing it.
Round 2: Coding — Lock-Free Queue
The second round focused much more heavily on concurrency.
The task was to implement a lock-free queue supporting concurrent enqueue and dequeue operations without using traditional locks.
I started from the concept of CAS (Compare-And-Swap) and discussed a linked-list-based lock-free queue.
For enqueue, the idea is to use CAS to update the tail. For dequeue, CAS is used to move the head forward.
The interviewer then asked about the ABA problem.
This is where concepts such as tagged pointers and hazard pointers become relevant. The important part is recognizing that seeing the same pointer value does not necessarily mean the underlying object has remained unchanged.
Another interesting follow-up was:
How does Python's GIL affect your lock-free implementation?
This is a topic worth preparing if you are interviewing with Python.
The GIL should not simply be treated as a replacement for proper concurrency control. It does not eliminate the need to reason about concurrency, and it does not solve synchronization problems in multiprocessing scenarios.
The interviewer then asked:
What if the queue needs to support priorities?
I discussed using a Skip List as a possible underlying structure and explained that a complete lock-free priority queue would be significantly more complicated.
I was transparent about the implementation complexity and gave a conceptual solution rather than trying to fake a complete implementation within the remaining interview time.
The interviewer agreed with the direction and moved on.
My main takeaway from this round:
You need to understand concurrency beyond the API level.
Round 3: System Design — Web Crawler
The third round was a System Design interview focused on building a Web Crawler.
Before jumping into the architecture, I clarified several requirements:
- Single-domain or multi-domain crawling?
- Expected number of URLs?
- Maximum crawl depth?
- Do we need to respect robots.txt?
- How will the crawled data be stored and consumed?
URL Scheduling
I proposed using a priority queue to manage URLs waiting to be crawled.
For deduplication, a Bloom Filter can significantly reduce memory usage compared with maintaining a complete HashSet, at the cost of allowing a very small false-positive rate.
Fetching Layer
For the fetching layer, I suggested asynchronous requests with concurrency control.
One particularly important detail was per-host rate limiting.
A crawler should not simply maximize its own throughput. It also needs to respect the capacity of the websites it is crawling.
The design should also account for robots.txt and crawl-delay requirements.
Storage Layer
Raw HTML could be stored in object storage such as S3, while parsed structured data could go into a database.
URL state and crawl scheduling information could be stored in a distributed key-value store to make scheduling and recovery easier.
The interviewer then introduced several additional constraints.
What if dynamically generated URLs cause URL explosion?
We can normalize URLs before deduplication, remove meaningless parameters where appropriate, and impose a maximum crawl limit per domain.
What if fetching a URL times out?
Use exponential backoff with a maximum retry count. URLs that continue to fail can be moved into a dead-letter queue with failure information recorded for later analysis.
How would you implement graceful shutdown?
After receiving a shutdown signal, the crawler should stop accepting new work, allow in-flight requests to finish, persist the current queue state, and restore unfinished work when the service starts again.
This round felt particularly representative of Anthropic's engineering philosophy.
If you only focus on making the crawler faster, it is easy to forget about robots.txt, per-host rate limiting, and the impact your system has on external services.
Those details demonstrate a more responsible engineering mindset rather than simply optimizing for throughput.
Round 4: Behavioral — AI Safety, Teamwork, and Why Anthropic
The final round was behavioral, but it was probably the round with the deepest follow-up questions.
AI Safety
One question was essentially:
What do you think is the most serious safety risk in current AI systems, and what can you do about it as an engineer?
There is no single correct answer to this question.
What matters is whether you can discuss a concrete risk scenario, explain how the risk could occur, and propose engineering-level mitigations.
For example, I discussed ideas such as:
- Output monitoring
- Abuse detection
- Rate limiting
- Architectural safeguards
- Continuous monitoring and alerting
The key is not memorizing an AI Safety answer. The interviewer wants to see whether you have genuinely thought about safety as part of engineering.
Teamwork and Technical Disagreements
Another question focused on a situation where I had a strong disagreement with a teammate.
The follow-ups went deeper:
- Why did you make that decision?
- Did you understand the other person's concerns?
- How was the final decision made?
- What would you do differently if you could do it again?
Anthropic does not seem particularly interested in a generic answer such as “we eventually reached consensus.”
They want to understand your decision-making framework, whether you can maintain your own judgment during disagreement, and whether you are genuinely willing to listen to other perspectives.
Why Anthropic / Why This Role?
This question also felt more important at Anthropic than at many other companies.
The interviewer is trying to understand whether you genuinely care about what Anthropic is building or simply see it as another strong software engineering opportunity.
If you are preparing for Anthropic, I would recommend learning about Constitutional AI, the Responsible Scaling Policy, AI Safety, and Anthropic's broader research and product direction.
Anthropic SWE VO Preparation Guide
Coding
- Rate Limiter
- Lock-Free Queue
- Thread Pool
- asyncio
- CAS and concurrency primitives
- Race conditions
- Distributed concurrency
- Python GIL
System Design
- Web Crawler
- Distributed Cache
- Real-Time Systems
- Rate Limiting
- Multi-Tenant Systems
- High-Concurrency Services
Behavioral
- AI Safety
- Technical disagreements
- Ownership
- Project failures
- Cross-team collaboration
- Why Anthropic?
- Responsible engineering
For coding interviews in particular, don't prepare only for the initial implementation.
Get comfortable with follow-up questions such as:
What if the system scales?
What if Redis goes down?
What if requests become highly concurrent?
What if this component becomes the bottleneck?
What are the trade-offs?
This is one of the biggest differences I noticed during the Anthropic VO.
FAQ
Is the Anthropic VO completed in one day?
It can be completed in one day, although some candidates may have the interviews split across two days depending on scheduling, time zones, and recruiter availability. Each round is typically around 60 minutes.
How long does it take to hear back after the Take-Home Project?
The timeline can vary by role and hiring batch. Candidates may wait one or more weeks after submitting the Take-Home Project before receiving the VO invitation.
What tools are used during the VO?
The interviews generally use a shared coding environment such as CoderPad or a similar collaborative editor. The emphasis is on explaining your reasoning while coding rather than simply submitting a solution through a LeetCode-style platform.
How is Anthropic's Behavioral interview different from Amazon's Leadership Principles?
Amazon has a clearly defined Leadership Principles framework. Anthropic does not follow the same type of fixed framework.
Instead, candidates should be prepared to discuss AI Safety, technical disagreements, teamwork, ownership, responsible engineering, and Why Anthropic. The follow-up questions can feel more conversational and exploratory rather than like a checklist.
How deeply should I prepare for concurrency?
I would recommend being comfortable with Rate Limiters, Lock-Free Queues, Thread Pools, asyncio, CAS, race conditions, synchronization, distributed concurrency, and the Python GIL.
You should be able to implement basic versions while also explaining the trade-offs and failure modes in distributed environments.
Can I interview at Anthropic without an AI Safety background?
Yes. You do not need to be an AI Safety researcher.
However, you should be able to demonstrate that you have seriously thought about AI safety from an engineering perspective. Reading Anthropic's public materials on Constitutional AI and the Responsible Scaling Policy can help you understand the company's approach.
Why is per-host rate limiting important in a Web Crawler interview?
Because a good crawler should not optimize only for its own throughput. It should also consider the impact it has on external websites.
Per-host rate limiting, robots.txt compliance, retries, and graceful degradation demonstrate that you are thinking about responsible system design rather than simply maximizing performance.
Final Takeaways
After completing all four rounds, the biggest difference I noticed between Anthropic and traditional big-tech interviews is that Anthropic seems less interested in whether you can quickly recall a standard solution and much more interested in how you reason through an incomplete problem.
This is especially obvious in System Design and AI Safety.
The initial problem may not look extremely difficult. The challenge is that the interviewer keeps changing the constraints.
When preparing, don't just memorize system design templates. For every problem, ask yourself:
- What happens if traffic increases by 10x?
- What happens if a dependency fails?
- What happens if users abuse the system?
- What happens if the design itself introduces a new security risk?
If you can reason through those questions clearly, you'll be much better prepared for the Anthropic VO.
For anyone preparing for Anthropic or other AI-company SWE interviews, I would prioritize concurrency, distributed systems, System Design, and AI Safety-related behavioral preparation.
InterviewShow also covers interview preparation for Anthropic, OpenAI, Google, and other top technology companies, including Rate Limiter follow-ups, Web Crawler System Design, concurrency questions, and AI Safety behavioral interviews.
If you're preparing for an upcoming interview and want structured one-on-one preparation, you can learn more at InterviewShow.
Top comments (0)