About the authors // Yong is an ML researcher and former Astra Fellow.Joseph is a Research Program Manager at Constellation, the nonprofit that runs the Astra Fellowship and other AI safety programs.This post reflects our personal opinions, not those of any organization.
There's excellent prep materials out there for traditional technical interviews, product/program/project interviews, and coding tests—and a real gap in guidance for researchers going through an AI safety-specific interview for the first time. This post aims to fill that gap.
This post is for you if you're already in an interview pipeline for an AI safety role. It's not a guide to landing the interview. This post is not specific to independent safety orgs or frontier labs, but is intended to be broadly useful across the spectrum of full time AI safety role interviews.
Interview processes vary significantly by org and team
The first thing to know: AI safety interviews are not standardized the way traditional software engineering interviews are. Each organization, and often each team within an organization, often has its own approach. Some organizations only have one or two rounds being safety-focused, whereas some have the entire pipeline evaluating your AI safety knowledge and expertise.
First, you should explicitly ask how to best prepare for the interviews. Many recruiters will happily provide you with the guidelines. Another highly useful thing you can do is talk to people who've been through it. If you’ve gotten to the interview stage, you’ve already passed through the biggest resume screening filters. That’s a meaningful signal that you’re in the running for the role. We expect people who could advise you to respond to these requests far more often than if you’re asking how to get through a resume screen stage.
Reach out to researchers at the org you're interviewing with and ask for 15 minutes. Try your university alumni networks, LinkedIn second-degree connections (ask a direct connection for a warm intro). Many people in this field may be willing to share what they're allowed to share - if they work full time in AI safety, they probably want more people to work full time in AI safety!
If you're in a fellowship program at Constellation or elsewhere, your research manager and mentors often have direct knowledge of specific org processes and may be able to look into intel or introductions relevant to your interviews.
Prep for AI safety-specific round(s)
This is the part with the least existing guidance, so we'll spend the most time here.
Safety-specific interview content varies by org and team, but in our experience it clusters around a few recognizable question types. Knowing which type you're in — and what the interviewer is actually watching for — changes how you should respond.
Below are some possible directions for safety-specific assessments or interviews, and some ideas on what to consider when prepping for one. You could reasonably expect to face questions from any or all of these categories. We recommend asking the recruiter or hiring manager (or your connections on/around the team) for the role which ones are most relevant, or prepping for all of them if you can’t get further info.
Research brainstorms
"How would you design an evaluation to detect deceptive alignment?"
"What experiments would you run to test whether a model has internalized a value versus learned to perform it?"
You could be given an open-ended, underspecified problem and asked to think through it in real time. Importantly: there isn’t necessarily a correct answer. The team often wants to see how you decompose a problem, how you scope. A strong response could include elements of:
restates your interpretation of the question before diving in
identifies what you'd need to know first
proposes a concrete first move, and names what you're uncertain about.
A weak response recites correct concepts rote but doesn't demonstrate your own thinking and experience about the topic. Materials like Generative AI System Design Interview might be helpful and complementary for some of these open-ended safety design questions.
Research taste questions
"What alignment problems do you think are most neglected right now?"
"What would you work on if you joined this team?"
Research taste is an interesting one, defined and debated different ways by different researchers. You might want to have prepared one or two specific research directions you can defend from first principles, especially those related to projects that you have worked on. You might be asked questions such as what would change your mind about your research hypotheses, design decisions, or conclusions, and you should be prepared to hold your position when pushback/debate with the interviewer isn't compelling, and updating when it is. The ability to tell the difference, in real time, is often itself what's being evaluated.
It’s possible that your research interests and instincts are being evaluated in terms of how aligned with the team’s current directions and taste are, so it can also be helpful to be familiar with the specific team’s research agenda and recent work.
Deep-diving or presenting your own work
"Walk me through your project and what you'd do differently in retrospect."
“What additional experiments would you have liked to have done with more time?”
Our main advice here is to know your own work (projects, papers, writing and ideas) very well. Study your own research, and practice communicating it to others. Be prepared to explain, defend and critique it: design decisions, surprising results, assumptions, the research question, the methods, and the next experiment you would run in the project (because your project wasn’t a task to be completed, it’s part of a broader landscape of possible research directions).
Prep for non-safety-specific technical round(s)
Even for roles on dedicated safety teams, most interview processes include standard technical assessments. Prepare for these as you would for any research scientist or ML engineer role:
ML fundamentals (training dynamics, optimization, architecture basics)
Being well prepared for a behavioral interview can be a real differentiator in a technical interview process, including in AI safety interviews. Technical interviews are not just technical. You're being evaluated on how you communicate — which is a signal for what they can expect from you not just as an individual researcher, but a teammate, collaborator, communicator, and direct report to a manager.
Behavioral prep in this context means: communicating your knowledge and specifically about your own research and experience clearly to an important audience, handling pushback without either caving or defending reactively, and being able to retell your thinking out loud, under the pressure of the interview setting. These are skills, and they're learnable.
Mock interviews with a real human — a peer, mentor, or research manager — are more useful here than AI practice. Human interviewers behave significantly differently from AI.
About the authors // Yong is an ML researcher and former Astra Fellow. Joseph is a Research Program Manager at Constellation, the nonprofit that runs the Astra Fellowship and other AI safety programs. This post reflects our personal opinions, not those of any organization.
There's excellent prep materials out there for traditional technical interviews, product/program/project interviews, and coding tests—and a real gap in guidance for researchers going through an AI safety-specific interview for the first time. This post aims to fill that gap.
This post is for you if you're already in an interview pipeline for an AI safety role. It's not a guide to landing the interview. This post is not specific to independent safety orgs or frontier labs, but is intended to be broadly useful across the spectrum of full time AI safety role interviews.
Interview processes vary significantly by org and team
The first thing to know: AI safety interviews are not standardized the way traditional software engineering interviews are. Each organization, and often each team within an organization, often has its own approach. Some organizations only have one or two rounds being safety-focused, whereas some have the entire pipeline evaluating your AI safety knowledge and expertise.
First, you should explicitly ask how to best prepare for the interviews. Many recruiters will happily provide you with the guidelines. Another highly useful thing you can do is talk to people who've been through it. If you’ve gotten to the interview stage, you’ve already passed through the biggest resume screening filters. That’s a meaningful signal that you’re in the running for the role. We expect people who could advise you to respond to these requests far more often than if you’re asking how to get through a resume screen stage.
Reach out to researchers at the org you're interviewing with and ask for 15 minutes. Try your university alumni networks, LinkedIn second-degree connections (ask a direct connection for a warm intro). Many people in this field may be willing to share what they're allowed to share - if they work full time in AI safety, they probably want more people to work full time in AI safety!
If you're in a fellowship program at Constellation or elsewhere, your research manager and mentors often have direct knowledge of specific org processes and may be able to look into intel or introductions relevant to your interviews.
Prep for AI safety-specific round(s)
This is the part with the least existing guidance, so we'll spend the most time here.
Safety-specific interview content varies by org and team, but in our experience it clusters around a few recognizable question types. Knowing which type you're in — and what the interviewer is actually watching for — changes how you should respond.
Below are some possible directions for safety-specific assessments or interviews, and some ideas on what to consider when prepping for one. You could reasonably expect to face questions from any or all of these categories. We recommend asking the recruiter or hiring manager (or your connections on/around the team) for the role which ones are most relevant, or prepping for all of them if you can’t get further info.
Research brainstorms
"How would you design an evaluation to detect deceptive alignment?"
"What experiments would you run to test whether a model has internalized a value versus learned to perform it?"
You could be given an open-ended, underspecified problem and asked to think through it in real time. Importantly: there isn’t necessarily a correct answer. The team often wants to see how you decompose a problem, how you scope. A strong response could include elements of:
A weak response recites correct concepts rote but doesn't demonstrate your own thinking and experience about the topic. Materials like Generative AI System Design Interview might be helpful and complementary for some of these open-ended safety design questions.
Research taste questions
"What alignment problems do you think are most neglected right now?"
"What would you work on if you joined this team?"
Research taste is an interesting one, defined and debated different ways by different researchers. You might want to have prepared one or two specific research directions you can defend from first principles, especially those related to projects that you have worked on. You might be asked questions such as what would change your mind about your research hypotheses, design decisions, or conclusions, and you should be prepared to hold your position when pushback/debate with the interviewer isn't compelling, and updating when it is. The ability to tell the difference, in real time, is often itself what's being evaluated.
It’s possible that your research interests and instincts are being evaluated in terms of how aligned with the team’s current directions and taste are, so it can also be helpful to be familiar with the specific team’s research agenda and recent work.
Deep-diving or presenting your own work
"Walk me through your project and what you'd do differently in retrospect."
“What additional experiments would you have liked to have done with more time?”
Our main advice here is to know your own work (projects, papers, writing and ideas) very well. Study your own research, and practice communicating it to others. Be prepared to explain, defend and critique it: design decisions, surprising results, assumptions, the research question, the methods, and the next experiment you would run in the project (because your project wasn’t a task to be completed, it’s part of a broader landscape of possible research directions).
Prep for non-safety-specific technical round(s)
Even for roles on dedicated safety teams, most interview processes include standard technical assessments. Prepare for these as you would for any research scientist or ML engineer role:
These rounds are similar to rounds for non-safety research roles. For instance, see Silvio Sapora's ML Job Interviews: The Ultimate Guide for one helpful rundown.
Prep for behavioral side
Being well prepared for a behavioral interview can be a real differentiator in a technical interview process, including in AI safety interviews. Technical interviews are not just technical. You're being evaluated on how you communicate — which is a signal for what they can expect from you not just as an individual researcher, but a teammate, collaborator, communicator, and direct report to a manager.
Behavioral prep in this context means: communicating your knowledge and specifically about your own research and experience clearly to an important audience, handling pushback without either caving or defending reactively, and being able to retell your thinking out loud, under the pressure of the interview setting. These are skills, and they're learnable.
Mock interviews with a real human — a peer, mentor, or research manager — are more useful here than AI practice. Human interviewers behave significantly differently from AI.
Some people have found Cracking the Behavioral Interview or Mastering Behavioral Interviews useful preparation.
Further reading
On interview processes:
On what hiring managers want:
On empirical alignment research: