Anthropic Staff Software Engineer, Infrastructure Interview Experience
Anthropic · Staff · Software Engineer
I went in expecting a multithreaded DSA question, and instead they dropped me into a full project with image files where I had to Google a library on the spot. End of the day, I did not even have one image converted.
Interview process
I applied on the careers site with no referral, and the recruiter was in my inbox quickly, so the whole process started fast. The loop was a 15-minute recruiter call, then a phone design round, then a five-round onsite with system design, coding, project deep dive, behavioral, and a separate culture round. Everybody was responsive and genuinely nice, and the process felt way more efficient than most big tech loops, but the bar also felt extremely high. The AI-looking technical questions were mostly normal infra questions if I abstracted them correctly, but the coding round blindsided me because it was a practical project exercise, not the multithreaded coding problem I had prepared for. I came out feeling good about everything except coding, and I ended up rejected with zero feedback.
Interview rounds · 7
- 1
Recruiter screen
BehavioralArtificial IntelligenceI had a very short recruiter screen that felt more motivation-heavy than a normal big tech intro call, and she even brought up comp right away.
Q1. Why Anthropic, and what actually motivates you here, especially around AI safety?
How they answeredI walked through my background and gave my why-Anthropic answer, but I was not really prepared for how much they cared about motivation and AI safety. The recruiter kept probing on what actually drives me here. She explicitly warned me that at Anthropic people can do really well technically and still get rejected on behavioral, which was a useful hint that this company takes that part seriously.
Follow-up questions- How interested are you in the safety side of the work?
Q2. Are you comfortable with the Staff comp band for this role?
How they answeredShe brought up money herself, which stood out because a lot of big tech recruiters do not do that this early. She said if I cleared the Staff bar the base would be around the low $400k range and framed it as non-negotiable for the level. My response was that I did not want to talk about money until I cleared the interviews.
Follow-up questions- For this level, the base is around $410k and they do not negotiate.
- 2
Phone screen
System DesignTechnicalMy phone screen was not coding. It was a very open-ended design round where I had to decompose an AI-looking prompt into a normal infra problem and drive the discussion myself.
Q1. Design an inference batching system with constrained compute and lots of incoming requests.
How they answeredI treated the AI piece as a black box and framed it as an infra batching problem. I talked through a single GPU that can handle 100 inputs at a time, a lot of incoming requests, and the tradeoff between batching more for utilization versus making callers wait. I used a message queue, then went into too few versus too many messages, queue failure modes, error handling, scale, and how to tie an async pipeline back to a synchronous response. I had to drive this round pretty hard.
Follow-up questions- Suppose you have a single GPU and it can process 100 inputs at a time.
- What are the tradeoffs around batching, latency, and utilization?
- How would you handle failures, scale, and error handling?
- If you queue requests asynchronously, how do you return the response to the same user synchronously?
- 3
Final / onsite round
System DesignTechnicalThe onsite system design was another AI-flavored problem that was really just a classic distributed systems question once I abstracted it correctly.
Q1. You have one huge file coming in over a constrained link. How do you distribute it to thousands of machines?
How they answeredI framed it like file sharing, not some magical AI problem. You have one huge file, maybe a model binary, coming in over a constrained link and you need to get it to thousands of hosts, where each host also has constrained in and out bandwidth. I walked through the tradeoff between sending to a few hosts quickly versus fanning out widely and slowing every connection, then how peers can relay once they have the file. We finished in about 40 minutes, and when I asked if he wanted more depth, he said he had what he needed.
Follow-up questions- Assume each machine has constrained incoming and outgoing bandwidth.
- What is the tradeoff between sending to fewer hosts faster versus splitting bandwidth across many hosts?
- 4
Final / onsite round
CodingTechnicalThe coding round was the biggest surprise in the whole process. It was a practical exercise in a real project environment, not the multithreaded algorithm question I had prepared for.
Q1. Implement an image-processing pipeline over the provided files in the project.
How they answeredThis was the round that got me. I expected multithreading or a web crawler style problem, but instead they dropped me into a project with test cases, image files, and a pipeline spec in another file. I had never done image processing before, so I burned a lot of time googling libraries and digging through sub-APIs just to find the right methods. I never even got one image fully working end to end.
Follow-up questions- Read the pipeline instructions from another file and apply step-by-step transformations to each image.
- Use a library to do operations like rotate, invert, recolor, crop, or format conversion.
- 5
Final / onsite round
Project DiscussionCross-FunctionalExecutionBehavioralThe project deep dive looked like a technical walkthrough on paper, but in practice it was mostly about how I operate with other people.
Q1. Walk me through a recent large-scale project you worked on.
How they answeredI picked a recent project that was large scale and pretty cross-functional. I spent about 20 minutes laying out the project, then most of the questions shifted away from tech choices and into how I got agreement, resolved conflicts, set goals, and what metrics I used to judge whether it was working. It felt like they were using the project as a vehicle to evaluate how I operate, not to quiz me on specific technologies.
Follow-up questions- How did you come to agreement with other teams?
- How did you resolve conflicts?
- What goals and metrics did you use?
- 6
Final / onsite round
BehavioralCross-FunctionalThe behavioral round was a standard big tech-style STAR interview, just run very methodically by a senior leader.
Q1. Tell me about a cross-functional conflict and how you handled it.
How they answeredI used standard STAR-style stories from cross-functional work and conflict management. The interviewer had a very set list and kept it structured, so I focused on being clear about the situation, what I did, and what I learned. It did not feel Anthropic-specific by itself, but it was obvious they cared a lot about the behavioral signal.
Follow-up questions- What did you learn from it?
- 7
Final / onsite round
BehavioralArtificial IntelligenceThe culture round was separate from behavioral and was the most Anthropic-specific part of the loop. It was explicitly about values, safety, and how I reason through moral tension.
Q1. Tell me about a time you had a moral conflict with the work.
How they answeredI used a prior chatbot project as my example. I said the moral tension for me was that tools like this can help users but can also contribute to people losing jobs, and I still had to work on it. They did not let me stay high level. They kept drilling into how I resolved that conflict inside myself, who I talked to in the organization, what those people actually said, and what specifically changed my mind. That was easily the hardest part of the loop.
Follow-up questions- How did you resolve that conflict within yourself?
- Who did you talk to?
- What was their role?
- What did they say that changed your mind?
Q2. How would you handle delivery pressure when there is a security or safety concern?
How they answeredI remember this one as more of a values tradeoff than a standard execution question. The framing was basically delivery versus a security issue, and what stuck with me was that they cared less about a canned answer and more about how I reasoned through it and whether that reasoning matched their values.
Follow-up questions- How do you make that choice?
- How would you explain it and rally the team?
Tips from the candidate
If I were doing it again, I would prep this like two interviews at once. For the technical side, do not get intimidated by the AI wrapper. A lot of it is just infra if you can abstract well and drive the round yourself. For the non-technical side, have real stories ready on motivation, safety, moral conflict, cross-functional disagreement, and how you make tradeoffs. Also, do not assume the coding round is DSA or even typical multithreading. Be ready for something practical where they care about how fast you can figure out unknowns in a messy environment.
Company culture
What stood out to me is that Anthropic is fast, responsive, and pleasant on the surface, but also pretty cutthroat. Recruiters move quickly, interviewers are nice, results come back fast, and they do not waste time, which I honestly appreciated. At the same time, they seem very willing to reject technically strong people if the behavioral or values signal is off. Compared with other hot AI companies, the speed felt similar, but Anthropic put way more explicit weight on motivation, safety, and culture fit. They also gave me zero feedback, and even the recruiter brought up comp unusually early, so it felt like a company that knows exactly what signal it wants and is optimized to move fast on it.