Applicants are invited to apply for the OpenAI Safety Fellowship 2026 in the United States. The OpenAI Safety Fellowship 2026 is open to international researchers, engineers, and practitioners who want to carry out independent research in AI safety and alignment. The fellowship will run from September 14, 2026 to February 5, 2027.
The OpenAI Safety Fellowship is a pilot program created to support the next generation of AI safety researchers. Fellows will work closely with OpenAI mentors and a cohort of other participants. The programme focuses on important safety questions for current and future AI systems.
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Successful applicants will receive a weekly stipend of $3,850, compute support of up to $15,000 per month, mentorship from OpenAI’s safety and alignment teams, and access to a shared workspace in Berkeley, California. Remote participation is also possible.
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OpenAI Safety Fellowship 2026 Summary
- Host Country: United States
- Study Abroad: Study in USA
- Host: OpenAI
- Category: Fellowships
- Eligible Countries: All Countries
- Reward: $3,850 Weekly Stipend | Up to $15,000 Monthly Compute Support | Mentorship | Workspace Access | Visa Support
- Deadline: May 3, 2026
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OpenAI Safety Fellowship 2026 Details
The OpenAI Safety Fellowship is designed for people who want to work on high-impact research related to AI safety and alignment. The programme welcomes applicants from many backgrounds, including computer science, cybersecurity, social science, privacy, human-computer interaction, mathematics, physics, and related fields.
OpenAI is especially interested in research that is technically strong, practical, and useful to the broader AI safety community. Fellows are expected to produce a major research output before the end of the fellowship. This may include:
- A research paper
- A benchmark
- A dataset
- Another important technical research output
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Priority research areas include:
- Safety evaluation
- AI ethics
- Robustness and reliability
- Scalable safety mitigations
- Privacy-preserving safety methods
- Agentic oversight
- High-severity misuse prevention
- AI alignment research
Fellows will work mainly with open-source AI models and public APIs. They will not have access to OpenAI’s internal systems.
The fellowship will mainly take place in Berkeley, California, where fellows can work from the Constellation shared workspace. However, remote work is allowed for participants who cannot relocate.
Potential Mentors
Selected fellows may receive mentorship from members of OpenAI’s safety and alignment teams, including:
- Chloé Bakalar
- Micah Carroll
- Tom Dupre la Tour
- Isak Czeresnia Etinger
- Tomek Korbak
- Jenny Nitishinskaya
- Jolene Parish
- Xiangyu Qi
- Dylan Sam
- Karan Singhal
- Jay Wang
- Kaiwen Wang
- Gabriel Wu
- Maja Trebacz
- Marcus Williams
- Jasmine Wang
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Eligibility Requirements for the OpenAI Safety Fellowship 2026
To apply for the OpenAI Safety Fellowship 2026, applicants must meet the following requirements:
- Be interested in full-time empirical AI safety and alignment research
- Be available to participate full-time for the entire fellowship period
- Be able to work approximately 40 hours per week from September 14, 2026 to February 5, 2027
- Have strong technical skills, especially in Python programming
- Be able to solve difficult and unclear technical problems
- Be able to make progress independently and work well in a team
- Be interested in one or more fellowship research areas
- Be willing to produce a substantial final research output
- Submit at least two references who can explain your strengths and experience
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You do not need to have:
- A PhD
- Previous machine learning work experience
- Published academic papers
Applicants from backgrounds such as physics, mathematics, computer science, cybersecurity, social sciences, and other quantitative disciplines are encouraged to apply.
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OpenAI Safety Fellowship 2026 Benefits
The OpenAI Safety Fellowship 2026 provides the following benefits:
- $3,850 per week stipend
- Compute support of up to approximately $15,000 per month
- Mentorship from OpenAI safety and alignment researchers
- Opportunity to work with leading experts in AI safety
- Access to a shared workspace in Berkeley, California
- Remote participation option
- Possible visa support for selected fellows
- API credits and additional research resources
- Opportunity to join a community of researchers and practitioners
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OpenAI can support selected fellows through different visa options, including:
- J-1 visa
- F-1 CPT (full-time only)
- F-1 OPT with approval from the Designated School Official (DSO)
- Other visa arrangements may also be considered
Required Documents for the OpenAI Safety Fellowship 2026
Applicants must prepare the following before starting the application:
- Completed online application form
- Detailed answers to application questions
- At least two references
- Information about your technical background and research interests
- Details about your preferred research area
- Visa information if applicable
Applicants are advised to save a copy of their responses before submission because answers cannot be edited after the application is submitted.
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How to Apply for the OpenAI Safety Fellowship 2026
Follow these steps to apply:
- Visit the official OpenAI Safety Fellowship application portal.
- Read the programme details and eligibility requirements carefully.
- Prepare your research interests, technical background, and references.
- Complete the online registration form.
- Answer all application questions with specific and detailed responses.
- Provide at least two references who know your work well.
- Submit your application before the deadline.
- If shortlisted, complete the technical assessment and interviews.
- Attend a final interview with a potential OpenAI mentor.
The selection process includes three stages:
- Review of application and references
- Technical assessments and interviews
- Final interview with possible mentors
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Deadline
The deadline to apply for the OpenAI Safety Fellowship 2026 is May 3, 2026 at 11:59 PM
Successful applicants will be notified by July 25, 2026.
Application LInk
