Updated by Anthropic candidates
AI Safety Fellow Interview Experience
The weirdest part was the final round: they dropped me into a Google Colab notebook with maybe 20 to 30 lines of skeleton Python and asked me to debug an actual LLM inference step, but you were explicitly not allowed to use an LLM.
Interview process
This process felt very different from most of the interviews I've done. There was no classic DS&A round at all. Instead, they tested raw coding implementation speed early on with database-style CodeSignal rounds, and then the final loop shifted hard into research thinking and a pretty fundamental understanding of LLMs. The most unusual part was the structure of the final: a 15-minute open-ended alignment brainstorm with almost no interviewer feedback, followed by a 55-minute Colab notebook exercise where I had to complete part of an LLM inference workflow. It also felt like a newer process when I did it, so I was mostly inferring what to expect from the emails rather than finding much info online.
- Online assessment
- Technical interview
- Final round
Interview tips
I'd prep for this by focusing less on textbook coding patterns and more on writing clean code fast under time pressure. For the coding rounds, be ready for implementation-heavy problems where passing tests and debugging edge cases matter more than clever tricks. For the final brainstorm, read as much as you can about Anthropic's safety and alignment research beforehand and have your own research ideas ready, because the question is open-ended and you may get basically no feedback in the moment. Also, the interview emails were actually pretty clear, so I'd study those carefully and make sure I understand the format before going in.
Company culture
My impression was that Anthropic was hiring for core thinking speed and research taste more than for polished interview-game performance. Even the coding rounds felt practical and implementation-heavy, like they wanted to see whether I could reason through systems and work with data fast, not just solve a trick problem. They also seemed pretty standardized and strict about no LLM use, which to me suggested they cared about seeing my unaided thinking process even for roles that would obviously use LLMs on the job. Because this fellowship was still relatively new when I interviewed, the whole thing also felt like an early-cohort process that was more specialized and less documented than a normal big-company loop.
Questions asked
Overview
The final loop had two very different pieces. First was a super short 15-minute research brainstorm with a potential mentor where I got almost no signal about whether I was going in the right direction. Then there was a 55-minute Colab-based LLM coding round that felt like debugging or completing part of an inference pipeline rather than doing a normal algorithm problem.
Question types asked
Specific questions asked
The discussion was broad and open-ended, around alignment rather than ethics in a generic sense. I talked through ideas around preventing bad actors from pushing the model toward bad use cases, detecting misalignment, and training models to behave more in line with the intended objectives. What made it hard was that the interviewer basically didn't give feedback while I was brainstorming, so I couldn't tell if I should keep pushing a line of thought or pivot. The email had said there was no right or wrong answer, and that was exactly how it felt.
In a provided Google Colab notebook, fill in the missing code to implement part of an LLM inference or output-processing step.
Complete the provided functions so the model output is processed correctly based on the interviewer's expectations.
This was done in a Google Colab notebook, and they explicitly told me ahead of time to test the environment and GPU so I wouldn't waste interview time on setup. The notebook already had around 20 to 30 lines of Python skeleton code, basically two function shells, and I had to fill in the missing technical details. The task was centered on inference and getting the LLM output into the right processed form. It felt almost like a debugging round for how an LLM works, which I hadn't seen before in an interview.
Get full access with a membership, or share your experience to try it free.
