Anthropic ML Engineer, Prompt Engineer Interview Experience
Anthropic · Machine Learning Engineer
The culture fit round was so deep it was almost like therapy for me. I talked about pushing back on executive pressure to launch on a timeline and raising it up to chief counsel to make sure the data was correct.
Interview process
Tl;dr is that the process was hard, thoughtful, and more advanced than a typical ML loop imo. I got in through someone in my network, then after I applied I went through a recruiter screen, a technical use-case screen, an MLOps round, a hiring manager round, and a three-part final panel with ML design, behavioral, and culture fit. The whole process felt very centered on real LLM work: MCP and tooling, long context windows, memory, reliability, enterprise deployment, and whether I actually know when AI makes sense versus plain ML. One thing that stood out is they let me use their LLM in the interview system, which honestly made it feel closer to the real job than most AI interviews I have done. The culture fit round was the most distinctive because it went deep on ethics, executive pressure, and EQ, and it honestly felt almost like a therapy convo.
Interview rounds · 5
- 1
Recruiter screen
BehavioralArtificial IntelligenceMachine LearningThe recruiter screen felt deeper than a normal recruiter call because they were not just checking fit and logistics, they were trying to figure out what AI and ML work I had actually done, what models I really used, and whether I understood their focus on safe development.
Q1. Why Anthropic, and why are you interested in this ML engineer prompt engineer role?
How they answeredI told them I was already using Claude Sonnet in proof-of-concept work, and it was performing better than a lot of the other models we were testing. From there it turned into a real conversation about what other platforms I liked, what specific models stood out to me, and what challenges I had run into. It felt like they were mapping my actual hands-on skills to the role, not just checking boxes.
Follow-up questions- What other platforms or models do you like?
- What challenges have you faced using them?
- What core skills have you really used that line up with this role?
Q2. What compensation range are you looking for?
How they answeredThey said comp would depend on where I landed on seniority after the interviews, but based on my background they put me in a 180k to 220k base range. I basically said if the base was there, everything else was a plus and asked about RSUs and performance bonuses. They did not really get into total comp yet and made it sound like that comes later if you clear the loop.
Follow-up questions- Based on your background, would a 180k to 220k base range make sense?
- 2
Technical round
TechnicalMachine LearningArtificial IntelligenceSystem DesignThe first technical screen dropped me straight into a practical tooling use case inside their system, and I could use their LLM while I talked through how I would make the workflow work in the real world.
Q1. Here is a tooling scenario where the model has to make a plan and execute a tool. How would you solve it?
How they answeredThey gave me a use case that felt like an MCP and tooling problem, basically an error or batch-script style flow where the model had to make a plan and execute a tool. I walked through how I would incorporate tooling into the agent framework, manage the context window, and think about long-running tasks so the workflow stayed reliable. They also wanted me to explain the surrounding stack and how I would work with other people around it.
Follow-up questions- How would you handle this through MCP?
- How would you manage the context window for long-running tasks?
- What parts of the stack would you use, and who else would you involve?
- 3
Technical round
TechnicalMachine LearningArtificial IntelligenceSystem DesignThe ML ops round was more advanced than a typical ML screen because it was not really about moving data or writing code from scratch, it was about performance tuning an LLM workflow around memory, context, and output consistency.
Q1. Given a simple PDF-based workflow with long context windows, how would you improve reliability and performance?
How they answeredI was basically talking through a PDF workflow and how I would make the interaction with the model more performance reliable. The focus was on long context windows, memory, chat history, timeouts, and making sure the output stayed efficient and consistent. I was not really rewriting code. It was more like showing that I understood what I was seeing and how I would tune the loop so it performed reliably.
Follow-up questions- How would you store memory and chat history?
- How would you avoid timeouts?
- How would you keep the output consistent without rewriting the whole code path?
- 4
Other round
BehavioralMachine LearningArtificial IntelligenceCross-FunctionalThe hiring manager round mixed background review with a lot of probing on why I chose AI or ML at all, whether people actually adopted what I built, and how I think about safety, governance, and collaboration.
Q1. Why did you use AI or machine learning on this problem, and how did you know it was the right use case?
How they answeredThey pushed me on the actual reason for using AI or ML, not just what I built. I explained ML as more prediction-oriented, while AI is more about solving for operational efficiency, automation, or enhancing an experience or product. Then I tied that back to whether there was a real business user in the loop, whether adoption actually happened, and how long it took to get something production ready. It felt like they wanted proof that I know when AI is warranted and when it is not.
Follow-up questions- What is AI versus machine learning to you?
- Was there a business user in the loop?
- Did people actually adopt it?
- How long did it take to get something production ready?
Q2. How do you think about AI safety, ethics, guardrails, and governance?
How they answeredI talked about safety, ethics, guardrails, and governance as part of the build, not something you tack on later. My sense was that they really care whether you can put controls around a real system and not just talk about ethics in the abstract. It also tied back to whether I had worked with feedback from real users and knew how to make the process safe and reliable.
Follow-up questions- How would you implement guardrails in practice?
- 5
Final / onsite round
System DesignBehavioralArtificial IntelligenceMachine LearningCross-FunctionalThe final panel had ML design, behavioral, and culture fit, and the culture fit round was the most unusual because an engineer pushed me on ethics, executive pressure, and emotional intelligence in a way I had never seen before.
Q1. Tell me about an ethical or policy concern you raised under pressure.
How they answeredI gave an example where I was under executive pressure to produce a certain data output on a certain timeline so something could launch with a bigger campaign. I said I had to push back, raise the concern up to counsel, and make sure the data was actually correct instead of just forcing it through. We also talked about the pressure the team felt.
Follow-up questions- Did you ever have executive pressure to produce a certain output or hit a timeline?
- How did your team react?
- How did you escalate it?
Q2. If a customer in a regulated industry wanted to use Claude APIs, how would you design the implementation?
How they answeredThey framed it like a real advisory case. I talked through how I would use Claude APIs for a specific industry, what I would recommend around hosting and API management, and how I would think about enterprise implementation. They also pushed on security, trust, compliance, and what changes if the environment is something like a government contractor. It was very practical and felt close to work I would actually be doing.
Follow-up questions- What hosting or service model would you recommend?
- How would you manage the APIs?
- What would change in a government contractor environment?
- How would you handle compliance, security, and trust?
Q3. Tell me about a past project and the hardest part of tuning or fine-tuning the model.
How they answeredThey wanted specifics on the business domain and what I had actually done, but the real thing they wanted was the pain. I talked honestly about how frustrating tuning and fine-tuning can be and that it is never as clean as people make it sound. We even had a little chuckle about it, because you really cannot fake that part if you have not done it for real.
Follow-up questions- What business domain was it in?
- What was the budget?
- How frustrating was the tuning process really?
Tips from the candidate
I would brush up on anything around enterprise deployment before going in. I would also spend real time on LLM gateways, MCP tooling, context-window management, long-running tasks, memory, and chat history, because that felt like the bread and butter. Be ready to talk concretely about safety, governance, and times you had to push back under pressure. And do not give them polished fake-success answers on tuning or fine-tuning. They want the learnings. I also felt their recruiters were very deliberate about leveling and comp, so I would go in expecting them to be polished on that side.
Company culture
They are truly hiring for safe and reliable AI, not just saying the words. Almost every round came back to human-in-the-loop workflows, business user feedback, guardrails, governance, and whether I understand the difference between an AI use case and a machine learning use case. The interviewers felt engaged and the questions felt tied to actual work, esp around enterprise implementation, API security, and performance tuning. Even the culture fit round was run by an engineer and tested collaboration and EQ pretty hard.