Anthropic AI Research Scientist interview typically runs 4 rounds: written fit responses, CodeSignal, phone screen, technical video interview. It usually takes a few weeks and feels split between fit and execution, with a high bar for polished performance.
$320K
Avg. Base Comp
$746K
Avg. Total Comp
4
Typical Rounds
2-4 weeks
Process Length
Our candidates report that Anthropic is unusually explicit about wanting both mission alignment and technical polish, and it’s not enough to be strong in only one lane. The written prompts seem to do a lot of early filtering: multiple candidates noted that the company cared deeply about why they wanted Anthropic specifically and why they were drawn to AI safety research. That tells us the team is looking for people who can connect their work to the company’s public-benefit framing without sounding rehearsed.
On the technical side, the recurring theme is clean execution under pressure. One candidate described the coding assessment as standard in difficulty but unforgiving of sloppiness, and the follow-up coding screen felt similar: not especially exotic, but high bar, high precision. What stands out is that Anthropic doesn’t seem to reward merely getting to the right answer; they want a polished solution with few mistakes and clear judgment.
The most distinctive signal comes later, where the conversation shifts from coding to research taste. Our candidates report being asked to evaluate an LLM on a toy task and reason about using generated data to expand that evaluation. That’s a strong clue that Anthropic values careful thinking about evaluation design more than flashy model knowledge. The people who do well here are the ones who can explain tradeoffs crisply, especially when the problem is small but the implications for model behavior are not.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Anthropic process.
The part that stood out most to me was that the process felt split between fit and execution. First I had to write responses to a few paragraphs, and they cared a lot about general fit, especially why I was interested in Anthropic and AI safety research. After that came the CodeSignal, which was the standard industry coding assessment. It wasn’t especially algorithmically hard, but it did feel like there was no room to be sloppy because they seemed to expect a very clean result to move forward.
After the coding assessment, I had a phone screen that was basically a straightforward coding question, but again the bar felt high and I got the sense you needed to nail it perfectly to advance. The technical interview after that was over video call and was more research-flavored than I expected: I was asked to evaluate LLMs on a simple toy task, and then to think about using LLMs to generate extra data for that evaluation. That part was interesting because it wasn’t about deep algorithmic knowledge so much as whether I could reason carefully about evaluation design and data generation. Overall the coding pieces were more time-pressured than difficult, while the later interview was more about judgment and how I think about model evaluation. I didn’t get an offer, so my main takeaway was to prepare for both the written fit questions and a very polished coding performance, not just one or the other.
Prep tip from this candidate
Be ready to explain your interest in Anthropic and AI safety research in writing, and practice a very clean CodeSignal-style coding round where small mistakes could block you. For the technical interview, think through how you would evaluate an LLM on a toy task and whether synthetic data would actually help or distort that evaluation.
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Topics based on recent interview experiences.
Featured question at Anthropic
What do you tell an interviewer when they ask you what your strengths and weaknesses are?
| Question | |
|---|---|
| Client Solution Pushback | |
| Impact Reflection | |
| 2nd Highest Salary | |
| Experiment Validity | |
| Bagging vs Boosting | |
| Merge Sorted Lists | |
| P-value to a Layman | |
| Button AB Test | |
| Weekly Aggregation | |
| String Shift | |
| Job Recommendation | |
| Friendship Timeline | |
| Bank Fraud Model | |
| Hurdles In Data Projects | |
| Network Experiment Design | |
| Nearest Common Ancestor | |
| Prime to N | |
| Radix Addition | |
| Random Bucketing | |
| Swipe Precision | |
| RMS Error | |
| Minimum Change | |
| Success Measurement | |
| Testing Price Increase | |
| Recurring Character | |
| Complete Addresses | |
| Non-Normal Probability Distribution | |
| Delivery Estimate Model | |
| Encoding Categorical Features |
Synthesized from candidate reports. Individual experiences may vary.
The process starts with written responses to a few paragraphs. Anthropic appears to place strong emphasis on general fit, especially why you want to work there and why you are interested in AI safety research.
Candidates then complete a standard industry coding assessment through CodeSignal. The problems were not described as especially algorithmically hard, but the expectation was a very clean performance with little room for mistakes.
Next is a phone screen centered on a straightforward coding question. The bar is high, and the experience suggests you may need to solve it very cleanly to move forward.
The final reported round was a video interview that was more research-oriented than expected. The candidate was asked to evaluate LLMs on a toy task and then reason about using LLMs to generate additional data for that evaluation, with an emphasis on careful judgment and evaluation design.