How I Beat 2026 Anthropic Interview: My Process & Questions

I’m a computer science master’s student (AI focus) at Boston University. From 2025 to 2026, I worked as a research assistant at Tin Lab, where my research focused on mechanisms for updating entity states in language models. This included conducting probing experiments and behavioral evaluation for multiple large language models, predicting and analyzing error patterns, and directly driving the development and maintenance of the core code framework for the project.

Last December, I applied for the SWE position at Anthropic and made it to the final round within 8 weeks. Here’s a recap and summary of my interview experience over the past two months, including the full interview process and common questions. I hope it helps!

I’m really grateful to Linkjob.ai for helping me pass my interview, which is why I’m sharing my interview questions and experience here. Having an undetectable AI coding interview copilot during the interview indeed provides a significant edge.

Anthropic Interview Process Timeline

After submitting my resume, I received an invitation for a recruiter chat a few days later, which marked the beginning of the formal interview process:

Online Assessment—12/27（90min）

Technical Coding Screen—01/16（~40min）

Technical Coding Interview—02/04（~60min）

Technical System Design—02/04（~60min）

Senior Behaviour Interview—02/04（~60min)

Offer Call—02/07 (Normally within 72 hrs)

Anthropic Interview Process

Online Assessment

Question 1:

Implement an LRU cache in Python. The question required production-level code—thread-safe, robust error handling, and complexity analysis in comments.

At first, I used OrderedDict, which made the code super clean. But then the interviewer asked me to implement it from scratch using a doubly linked list and a hash map. Updating the pointers during data eviction took me way longer than I expected.

Question 2:

Design a task management system with features like priority handling, worker assignment, dependency resolution, and cascading cancellation.

I used a directed acyclic graph (DAG) with topological sorting to solve it. Almost forgot to add cycle detection, but managed to squeeze it in during the last 9 minutes!

Phone Screen & Coding Round 1

This was a technical phone interview, and the problem was to find duplicate files. I had to create a file myself to write a test case, which involved first writing to the entire file and then reading it completely. Afterward, I implemented chunk-based reading. The next step was to write the code to group the files by their size. For the last 15 minutes, the interviewer asked me a lot of questions, such as the reasons for choosing a specific chunk size, which hashing algorithm to use and why, how to determine if the bottleneck is I/O or CPU, and how to get the I/O throughput, among other things.

Virtual Onsite

My VO consisted of 3 rounds, originally scheduled for 4-5.

Additional Coding

This round was about multi-threaded web crawlers. My approach for this part was to first implement the single-threaded solution, then handle the concurrency. I also needed to do a little extra processing for the URLs. The interviewer gave me hints along the way. This round involved a lot of follow-up questions, such as how I would implement multi-threading, how I would perform a crawl within a cluster, and how I would distribute URLs for a distributed crawl across multiple machines.

After the follow-up, the interviewer asked if I'd ever had a similar problem. I said yes, and that led to me having an extra coding round.

The new challenge was to implement a Python LRU cache. I was given an existing LRU cache implementation that did not properly handle Python's variable-length arguments (*args) and keyword arguments (**kwargs), and my task was to add this functionality. The follow-up question was how to make the LRU cache persistent. I spent too much time on serialization and didn't finish the code. I initially planned to use pickle, but the interviewer suggested using json, which led me down a different path. I regret not sticking with pickle as it would have allowed me to focus on the core logic.

Overall, the Anthropic interview process felt quite compact and fast-paced. The problems themselves weren't difficult, but they contained many tricky details and edge cases to handle.

After completing all of those steps, the next part of the process was to schedule a follow-up with the recruiter. We will probably be discussing my job interests and my compensation expectations.

Linkjob AI worked great and I got through my interview without a hitch. It's undetectable, and the interviewer did not find that I used it.

System Design

The question I got was to design an inference API for serving large language models. I think the key areas they were assessing included handling variable-length requests, managing GPU memory under concurrent requests, implementing a priority-based request queue, and supporting streaming responses. Since this was closely related to Anthropic's core business at the time, they went really deep into the details.

My answer focused on batching strategies—specifically, how to dynamically group requests with similar lengths to maximize GPU utilization, and how to decide when to flush the queue versus wait for more requests. I also discussed KV cache management and auto-scaling strategies. I proposed using queue depth weighted by the estimated token count as the signal for scaling, instead of just relying on raw GPU utilization. The reasoning was that even if GPU utilization looks fine, latency could already be out of control. The interviewer seemed to really agree with that point.

For this round, I think strong preparation is absolutely critical. If your answers are smooth and well thought out, it makes a great impression. Otherwise, it’s hard to recover in the later rounds.

Senior Culture Interview

This round mainly focused on AI safety, a core topic for Anthropic, and also behavioral questions. The discussion was very detailed and in-depth, with the interviewer asking probing questions and discussing the examples I provided.

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Interview Impressions

I found that Anthropic’s question bank is quite consistent; I felt like I had seen many of the problems before. The benefit of this was being able to prepare in advance, but the downside was that if I had seen them, it was highly probable that others had too. Therefore, the interviews demanded a high level of precision in my answers, leaving almost no room for error.

Another key takeaway was that in several rounds, the interviewers were very keen on asking follow-up questions. This meant that when I prepared, I couldn’t just grind problems and focus on basic functionality. I also needed to genuinely understand the underlying mechanisms and the trade-offs for different application scenarios.

Anthropic Interview Questions

In my preparation, I actually came across some other shared interview questions. They are definitely very valuable. I'd like to share them here.

Recruiter Call

• Why are you interested in Anthropic?

• What is your understanding of Anthropic's philosophy?

Technical Interview

• Questions related to file deduplication.

• Designing a deduplication solution to run in a production environment.

Coding

• Generating program start and end logs based on a trace.

Cultural Fit

• How would you handle a conflict between your personal values and your work?

• Describe an experience where you resolved a conflict at work.

System Design

• Design a product similar to ChatGPT Playground.

• Discuss a scalable approach from product requirements to implementation, with a focus on product perspective and full-stack technologies.

Technical Project

• What is your perspective on AI security?

• What tradeoffs have you made for security?

Anthropic Interview Preparation Strategies

Research Anthropic

When I started my preparation for Anthropic, I realized that understanding the company’s mission and values made a huge difference. According to Anthropic's official website, the company focuses on building reliable and ethical AI systems that put safety at the frontier. Their work centers on safety, transparency, and minimizing bias. I spent time reading their official website, checking out research papers, and looking at their recent projects like "Values in the Wild" and "Agentic Misalignment." These resources helped me see how my own skills and interests matched their goals.

I also explored their product documentation and learned about their flagship product, Claude AI. This gave me a better sense of what the team values in terms of technical skills and ethical thinking. I made sure to reflect my passion for AI safety and ethics in my application and interviews.

Build AI/ML Skills

To stand out in the Anthropic interview process, I focused on sharpening my AI/ML and coding skills. I practiced building ml models, especially those related to trust and safety. I reviewed ml algorithms, llm architectures, and deep learning concepts. I also worked on coding challenges in Python and SQL, since these are often required for technical roles.

Here’s how I structured my preparation:

• I reviewed ml algorithms and practiced explaining how they work.

• I built small llm projects and contributed to open-source AI safety work.

• I used preparation material like Grokking courses and mock interviews to get ready for coding and system design rounds.

• I made sure my technical skills included both strong coding and a deep understanding of ml and llm concepts.

Tip: I also highlighted my teamwork and research experience, since Anthropic values collaboration.

Anthropic Interview Application Tips

Resume for Anthropic

I wanted to show the team that I understood their mission and had the right skills. So I focused on making my resume clear and direct. I used bullet points to highlight my achievements, and made sure every line connected to the job I wanted.

Here’s what worked for me:

• I used action verbs like "built," "designed," and "led."

• I included numbers to show impact, like "improved model accuracy by 15%."

• I tailored my resume for each job at Anthropic. I matched my skills to the job description.

• I kept my resume to one page. I removed anything that did not help me get hired at Anthropic.

• I added a section for projects. I described my work on AI safety and machine learning.

Anthropic Technical Interview Prep

Coding and Algorithms

Most coding interviews at Anthropic used Python. So I practiced in Jupyter Notebooks and worked with APIs, since these tools often came up in the interview.

I found that Anthropic’s coding interviews did not focus on tricky algorithm questions or advanced data structures. Instead, I saw a lot of LeetCode medium-level problems. The interviewers wanted to see how fast and accurately I could write code. They cared more about my coding speed and correctness than about Big O notation or complex algorithms. I spent time solving practical coding challenges and made sure I could implement classes with public APIs quickly.

Here’s what helped me most:

• I practiced writing clean, correct code in Python.

• I focused on ml and llm problems that matched real-world scenarios.

• I reviewed how to build and test classes with public APIs.

System Design

System design interviews at Anthropic felt different from other tech companies. I did not see questions about huge, complex systems. Instead, the interview focused on ml and llm system design. I explained how I would build safe, reliable ml systems and how I would handle edge cases.

I prepared by:

• Sketching out simple ml and llm system designs on paper.

• Reviewing common system design interview questions for ml and llm roles.

Anthropic Interview Behavioral and Ethics Rounds

Anthropic Interview Questions

When I reached the behavioral part of the Anthropic interview, I knew I had to prepare for more than just technical skills. Anthropic interview questions in this round focused on my past experiences, teamwork, and how I handled tough situations. The interviewers wanted to see if my values matched their mission. They asked behavioral questions about times I worked with others, faced challenges, or made decisions under pressure.

I noticed that Anthropic interview questions often explored my views on ethical ai. They wanted to know how I thought about fairness, accountability, and transparency. Sometimes, they gave me ethical dilemmas and asked how I would build ethical and safe ai. I had to show that I understood ai safety and ethics and could talk about ai ethics and safety discussions in my work.

I learned that behavioral interviews at Anthropic also checked my communication style. They wanted clear, concise answers. I made sure to show my awareness of current debates in ethical ai and how I use these ideas in my projects.

Final Preparation

Before my final Anthropic interview, I always take time to review and reflect on my journey. I want to make sure my preparation covers every angle. Here’s the checklist I follow to help candidates like me feel ready for any ml or job interview at Anthropic:

1. I research Anthropic’s mission, values, and recent projects. This helps me align my answers with the company’s goals and show I care about the job.

2. I practice mock interviews with friends, mentors, or AI tools.Including testing Linkjob.ai with friends before the formal interview to ensure everything goes smoothly and without a hitch during the actual interview. These sessions help candidates build confidence and simulate the real ml interview environment.

3. I review technical concepts and ml skills using online resources. I focus on the topics that come up most often in job interviews.

4. I prepare thoughtful questions about the role and company. This shows I am genuinely interested in the job and want to learn more.

5. I reflect on my past experiences using the STAR method. This helps me structure my answers for behavioral questions and makes my stories clear.

6. I use AI tools for real-time feedback and resume checks. These tools help candidates improve their performance and make their job applications stronger.

7. I reach out to current employees for referrals. This step can give candidates an extra boost in the ml job process.

8. I optimize my resume for each job. I use action verbs, numbers, and make sure it passes ATS checks.

9. I practice answering common behavioral questions. I ask for feedback to refine my responses.

10. I study the interview structure. I want to know what types of questions to expect in ml and job interviews.

11. I plan my outfit and practice good body language. First impressions matter in every job interview.

12. I reflect on my preparation and past interviews. I look for ways to improve and boost my confidence.

FAQ

How long did it take you to prepare for the Anthropic interview?

I spent about two weeks getting ready. I practiced coding, reviewed machine learning concepts, and did mock interviews.

What makes Anthropic's interviews unique or particularly challenging?

A key difference is the strong focus on AI safety and ethical alignment. You'll need to be prepared for deep, probing questions about these topics, as well as your personal values and how you've handled trade-offs in past projects. The interviewers also tend to ask a lot of follow-up questions, so it's not enough to just know the answer; you need to genuinely understand the underlying principles and concepts.

What should I do if I get stuck during an interview?

• Ask for clarification if needed.

• Use the STAR method to organize your answer.

See Also

Top 20 Real Anthropic Interview Questions I Compiled for 2026

My 2026 Anthropic SWE Interview Experience and Questions

Anthropic Coding Interview: My 2026 Question Bank Collection

How I Practiced Anthropic Codesignal and Passed the Interview

I Compiled Anthropic Concurrency Interview Questions for 2026