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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Real interview reports from people who went through the Anthropic process.
Anthropic reached out and I decided to move forward with the AI Research Scientist process after spending time at another FAANG company. The process was longer than I expected and involved several distinct stages. I started by submitting three references and a research essay, which felt like a substantial upfront commitment but helped them understand my background before the technical work began.
Next came two online assessments. The first was an engineering-focused CodeSignal task—not your standard LeetCode algorithmic problem but something more applied, like building an extensible in-memory database system. This tested both implementation skills and design thinking. The second assessment felt similar in scope and difficulty. I had some time to prepare between them, which helped me adjust my approach.
After clearing the OAs, I moved into a technical take-home phase with AI components, followed by review rounds where their team examined my solutions. This was thorough and felt like they were really evaluating depth rather than just right-or-wrong answers. The final stage was a 15-minute research discussion interview where they asked me about my past research experience, what I'd worked on, and how I thought about problems in the space. It was conversational but clearly assessing my research maturity and fit.
Ultimately I didn't receive an offer. Looking back, I think the research discussion round was where things didn't click as well as they could have, and the overall process is genuinely selective. It's worth noting that Anthropic seems to weight the research essay and discussion heavily, so if you're applying, spend real time on both of those rather than assuming pure technical execution will carry you through.
Prep tip from this candidate
Focus heavily on your research essay and be ready to discuss your past research work in depth—the final research discussion interview and essay submission appear to be as important as the technical assessments. Don't just prepare coding; prepare to articulate your research philosophy and experience clearly.
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Sourced from candidate reports and verified by our team.
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 | |
| Blogging Platform Schema | |
| Impact Reflection | |
| 2nd Highest Salary | |
| Experiment Validity | |
| Merge Sorted Lists | |
| First to Six | |
| Employee Salaries (ETL Error) | |
| Bagging vs Boosting | |
| Scrambled Tickets | |
| P-value to a Layman | |
| 500 Cards | |
| Raining in Seattle | |
| Friendship Timeline | |
| String Shift | |
| Hurdles In Data Projects | |
| N-gram Dictionary | |
| Compute Deviation | |
| Lazy Raters | |
| Button AB Test | |
| Nearest Common Ancestor | |
| Weekly Aggregation | |
| Job Recommendation | |
| Minimum Change | |
| Impression Reach |
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.
