Anthropic
Anthropic AI Safety Fellow, US
Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Note: this is our US job posting. You can find our UK and Canada job postings on our careers page. Please apply by August 17! Responsibilities:
The Anthropic Fellows Program is an external collaboration program focused on accelerating progress in AI safety research by providing promising talent with an opportunity to gain research experience. The program will run for about 2 months, with the possibility of extension for another 4 months, based on how well the collaboration is going. Our goal is to bridge the gap between industry engineering expertise and the research skills needed for impactful work in AI safety. Fellows will use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). Fellows will receive substantial support - including mentorship from Anthropic researchers, funding, compute resources, and access to a shared workspace - enabling them to develop the skills to contribute meaningfully to critical AI safety research. We aim to onboard our next cohort of Fellows in October 2025, with later start dates being possible as well. What To Expect
Direct mentorship from Anthropic researchers Connection to the broader AI safety research community Weekly stipend of $2,100 USD & access to benefits (including access to medical, dental, and vision insurance, a Health Savings Account, an Employee Assistance Program, and a 401(k) retirement plan) Funding for compute and other research expenses Shared workspaces in Berkeley, California and London, UK This role will be employed by our third-party talent partner, and may be eligible for benefits through the employer of record. Mentors & Research Areas
Fellows will undergo a project selection & mentor matching process. Potential mentors include: Ethan Perez Jan Leike Emmanuel Ameisen Jascha Sohl-Dickstein Sara Price Samuel Marks Joe Benton Akbir Khan Fabien Roger Alex Tamkin Nina Panickssery Collin Burns Jack Lindsey Trenton Bricken Evan Hubinger Our mentors will lead projects in select AI safety research areas, such as: Scalable Oversight: Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains. Adversarial Robustness and AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios. Model Organisms: Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise. Model Internals / Mechanistic Interpretability: Advancing our understanding of the internal workings of large language models to enable more targeted interventions and safety measures. AI Welfare: Improving our understanding of potential AI welfare and developing related evaluations and mitigations. For a full list of representative projects for each area, please see these blog posts: Introducing the Anthropic Fellows Program for AI Safety Research , Recommendations for Technical AI Safety Research Directions . You may be a good fit if you:
Are motivated by reducing catastrophic risks from advanced AI systems Are excited to transition into full-time empirical AI safety research and would be interested in a full-time role at Anthropic Have a strong technical background in computer science, mathematics, physics, or related fields Have strong programming skills, particularly in Python and machine learning frameworks Can work full-time on the fellowship for at least 2 months, and ideally 6 months Have or can obtain US, UK, or Canadian work authorization, and are able to work full-time out of Berkeley or London (or remotely if in Canada) Are comfortable programming in Python Thrive in fast-paced, collaborative environments Can execute projects independently while incorporating feedback on research direction We're open to all experience levels and backgrounds that meet the above criteria
you do not, for example, need prior experience with AI safety or ML. We particularly encourage applications from underrepresented groups in tech. Strong candidates may also have:
Experience with empirical ML research projects Experience working with Large Language Models Experience in one of the research areas (e.g. Interpretability) Experience with deep learning frameworks and experiment management Track record of open-source contributions Candidates need not have:
100% of the skills needed to perform the job Formal certifications or education credentials Interview process:
We aim to onboard our next cohort of Fellows in October 2025, with the possibility of later start dates for some fellows. Please note that if you are accepted into the October cohort, we expect that you will be available for several hours of mentor matching in October, although you may start the full-time program later. To ensure we can start onboarding Fellows in October 2025, we will complete interviews on a rolling basis until August 17, after which we will conduct interviews at specific timeslots on pre-specified days. We will also set hard cut-off dates for each stage - if you are not able to make that stage's deadline, we unfortunately will not be able to proceed with your candidacy. We've outlined the interviewing process below, but this may be subject to change. Initial Application and References
Submit your application below by August 17! In the application, we'll also ask you to provide references who can speak to what it's like to work with you.
Technical Assessment
You will complete a 90-minute coding screen in Python
As a quick note - we know most auto-screens are pretty bad. We think this one is unusually good and for some teams, give as much signal as an interview. It's a bunch of reasonably straightforward coding that involves refactoring and adapting to new requirements, without any highly artificial scenarios or cliched algorithms you'd gain an advantage by having memorized.
We'll simultaneously collect written feedback from your references during this stage.
Technical Interview
You'll schedule time to do a coding-based technical interview that does not involve any machine learning (55 minutes)
Final interviews
The final interviews consist of two interviews:
Research Discussion (15 minutes)
Brainstorming session with an Alignment Science team lead to explore research ideas and approaches Take-Home Project (5 hours work period + 30 minute review)
Research-focused project that demonstrates your technical and analytical abilities
In parallel, we will conduct reference calls.
Offer decisions
We aim to extend all offers by early October, and finalize our cohort shortly after. We will extend offers on a rolling basis and set an offer deadline
Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Note: this is our US job posting. You can find our UK and Canada job postings on our careers page. Please apply by August 17! Responsibilities:
The Anthropic Fellows Program is an external collaboration program focused on accelerating progress in AI safety research by providing promising talent with an opportunity to gain research experience. The program will run for about 2 months, with the possibility of extension for another 4 months, based on how well the collaboration is going. Our goal is to bridge the gap between industry engineering expertise and the research skills needed for impactful work in AI safety. Fellows will use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). Fellows will receive substantial support - including mentorship from Anthropic researchers, funding, compute resources, and access to a shared workspace - enabling them to develop the skills to contribute meaningfully to critical AI safety research. We aim to onboard our next cohort of Fellows in October 2025, with later start dates being possible as well. What To Expect
Direct mentorship from Anthropic researchers Connection to the broader AI safety research community Weekly stipend of $2,100 USD & access to benefits (including access to medical, dental, and vision insurance, a Health Savings Account, an Employee Assistance Program, and a 401(k) retirement plan) Funding for compute and other research expenses Shared workspaces in Berkeley, California and London, UK This role will be employed by our third-party talent partner, and may be eligible for benefits through the employer of record. Mentors & Research Areas
Fellows will undergo a project selection & mentor matching process. Potential mentors include: Ethan Perez Jan Leike Emmanuel Ameisen Jascha Sohl-Dickstein Sara Price Samuel Marks Joe Benton Akbir Khan Fabien Roger Alex Tamkin Nina Panickssery Collin Burns Jack Lindsey Trenton Bricken Evan Hubinger Our mentors will lead projects in select AI safety research areas, such as: Scalable Oversight: Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains. Adversarial Robustness and AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios. Model Organisms: Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise. Model Internals / Mechanistic Interpretability: Advancing our understanding of the internal workings of large language models to enable more targeted interventions and safety measures. AI Welfare: Improving our understanding of potential AI welfare and developing related evaluations and mitigations. For a full list of representative projects for each area, please see these blog posts: Introducing the Anthropic Fellows Program for AI Safety Research , Recommendations for Technical AI Safety Research Directions . You may be a good fit if you:
Are motivated by reducing catastrophic risks from advanced AI systems Are excited to transition into full-time empirical AI safety research and would be interested in a full-time role at Anthropic Have a strong technical background in computer science, mathematics, physics, or related fields Have strong programming skills, particularly in Python and machine learning frameworks Can work full-time on the fellowship for at least 2 months, and ideally 6 months Have or can obtain US, UK, or Canadian work authorization, and are able to work full-time out of Berkeley or London (or remotely if in Canada) Are comfortable programming in Python Thrive in fast-paced, collaborative environments Can execute projects independently while incorporating feedback on research direction We're open to all experience levels and backgrounds that meet the above criteria
you do not, for example, need prior experience with AI safety or ML. We particularly encourage applications from underrepresented groups in tech. Strong candidates may also have:
Experience with empirical ML research projects Experience working with Large Language Models Experience in one of the research areas (e.g. Interpretability) Experience with deep learning frameworks and experiment management Track record of open-source contributions Candidates need not have:
100% of the skills needed to perform the job Formal certifications or education credentials Interview process:
We aim to onboard our next cohort of Fellows in October 2025, with the possibility of later start dates for some fellows. Please note that if you are accepted into the October cohort, we expect that you will be available for several hours of mentor matching in October, although you may start the full-time program later. To ensure we can start onboarding Fellows in October 2025, we will complete interviews on a rolling basis until August 17, after which we will conduct interviews at specific timeslots on pre-specified days. We will also set hard cut-off dates for each stage - if you are not able to make that stage's deadline, we unfortunately will not be able to proceed with your candidacy. We've outlined the interviewing process below, but this may be subject to change. Initial Application and References
Submit your application below by August 17! In the application, we'll also ask you to provide references who can speak to what it's like to work with you.
Technical Assessment
You will complete a 90-minute coding screen in Python
As a quick note - we know most auto-screens are pretty bad. We think this one is unusually good and for some teams, give as much signal as an interview. It's a bunch of reasonably straightforward coding that involves refactoring and adapting to new requirements, without any highly artificial scenarios or cliched algorithms you'd gain an advantage by having memorized.
We'll simultaneously collect written feedback from your references during this stage.
Technical Interview
You'll schedule time to do a coding-based technical interview that does not involve any machine learning (55 minutes)
Final interviews
The final interviews consist of two interviews:
Research Discussion (15 minutes)
Brainstorming session with an Alignment Science team lead to explore research ideas and approaches Take-Home Project (5 hours work period + 30 minute review)
Research-focused project that demonstrates your technical and analytical abilities
In parallel, we will conduct reference calls.
Offer decisions
We aim to extend all offers by early October, and finalize our cohort shortly after. We will extend offers on a rolling basis and set an offer deadline