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.
Synthesized from 1 candidate report by our editorial team.
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Topics based on recent interview experiences.
Featured question at Anthropic
Would you think there was anything fishy about an A/B test run with 20 different variants where one is significant
| Question | |
|---|---|
| Concurrent LLM Serving | |
| Client Solution Pushback | |
| Pathfinder in Maze | |
| Your Strengths and Weaknesses | |
| LRU Cache 1 | |
| Impact Reflection | |
| Blogging Platform Schema | |
| 2nd Highest Salary | |
| Experiment Validity | |
| Merge Sorted Lists | |
| Bagging vs Boosting | |
| First to Six | |
| 500 Cards | |
| Scrambled Tickets | |
| P-value to a Layman | |
| Hurdles In Data Projects | |
| String Shift | |
| Compute Deviation | |
| Lazy Raters | |
| Button AB Test | |
| Raining in Seattle | |
| Weekly Aggregation | |
| Job Recommendation | |
| Friendship Timeline | |
| Impression Reach | |
| Bank Fraud Model | |
| Jars and Coins | |
| RMS Error | |
| Network Experiment Design |
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.