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Nick Perlman is an editor and video editor working with 80,000 Hours’ studio team. Based in New York, he has prior experience editing and producing video content for clients including America’s Test Kitchen and independent film projects, and now focuses on editing long‑form and short‑form content for 80,000 Hours’ podcasts and videos.
Julia Stoyanovich is an Institute Associate Professor of Computer Science and Engineering at NYU Tandon, Associate Professor of Data Science at the NYU Center for Data Science, and Director of NYUs Center for Responsible AI, with research focusing on responsible data management and the societal impacts of AI.
Social media content across YouTube, Instagram, and TikTok to grow AI x-risk awareness and build political momentum for a global pause.
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Inspiring India’s Middle‑Schoolers to pursue AI Safety, Governance, and X‑Risk Work
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Mordechai Rorvig is a science journalist and founder of Foom Magazine, an independent, ad-free publication covering research on AI safety and ethics. He was previously a staff writer at Quanta Magazine covering computer science and AI, a role he left in August 2022 to pursue independent journalism. He holds a master's degree in physics from the University of Wisconsin, where he conducted fusion device research, and a bachelor's degree in mathematics from the University of Texas. His freelance writing has appeared in Scientific American, Wired, Vice, New Scientist, Nautilus, and IEEE Spectrum, among others. He received a $110,000 grant from Open Philanthropy in November 2022 to support independent journalism on computer science, AI, and AI safety, and additional funding from the Long-Term Future Fund to launch Foom Magazine. He is also working on a book project titled "AI: How We Got Here—A Neuroscience Perspective," which explores connections between neuroscience research and the development of modern AI systems.
4-month stipend to research the mechanisms of refusal in chat LLMs
6-month salary and compute budget for continuing work on mechanistic interpretability for attention layers
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Clinical and research psychologist with over a decade of specialization in psychedelic medicine and integrative healthcare, with expertise in ibogaine and 5‑MeO‑DMT, neuropsychology, psychotherapy and psychedelic-assisted therapies.
6-months salary for researching “Framing computational systems such that we can find meaningful concepts." & Upskilling
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Research scientist at Truthful AI; previously a member of technical staff on Anthropic’s alignment team; currently on leave from a PhD in computer science at UC Berkeley, supervised by Stuart Russell.
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SFF main round did us dirty!
Dr. Ysaline Bourgine de Meder is a medieval historian who now serves as Head of Strategy at Effective Altruism Sweden, where she applies her analytical and strategic skills to AI governance and policy work.
Jugal Patel is the COO and Co-Founder of Leap Laboratories, based in San Francisco, California. According to his Crunchbase profile he previously worked at Balance.io as an Operations Lead and studied Business Administration and Finance at San Francisco State University.
Vanessa Kosoy is an AI alignment researcher based in Israel, currently serving as Director of AI Research at ALTER (Association for Long Term Existence and Resilience) and Research Lead at CORAL (Computational Rational Agents Laboratory), while pursuing a PhD in Mathematics at the Technion – Israel Institute of Technology under Shay Moran. She holds a BSc in Pure Mathematics from Tel Aviv University and an MSc in Computer Science from the Hebrew University of Jerusalem, and spent over 15 years in software engineering roles including algorithm engineer, R&D manager, and startup founder before transitioning to AI safety research full-time roughly a decade ago. She was previously a research associate at the Machine Intelligence Research Institute (MIRI) and has been funded by the Long-Term Future Fund (LTFF). Her research centers on the learning-theoretic AI alignment agenda, and she is best known for developing Infra-Bayesianism (with co-author Alex Appel), a mathematical framework for handling non-realizability in reinforcement learning, as well as Infra-Bayesian Physicalism (now called Formal Computational Realism), which addresses naturalized induction. She has been a mentor in the MATS (ML Alignment Theory Scholars) program, running a track focused on the learning-theoretic agenda, and is a prolific contributor to the AI Alignment Forum and LessWrong.
Junior Research Scholar at ILINA and a Researcher at the University of Cape Town African Hub on AI Safety, Peace and Security, conducting Africa‑centric model safety evaluations; he previously served as a Junior Research Fellow at ILINA (technical governance track), completed the AI Safety Fundamentals technical track and the ALX Software Engineering program, and holds a law degree from Kabarak University plus a postgraduate diploma from the Kenya School of Law.
Research Engineer at GDM, interested in Scalable Oversight and Human-AI Complementarity
Research Associate and Head of Policy at ILINA, working on governance of AI in Global South countries and the governance of AI agents, with prior experience as an AI Futures Fellow and visiting researcher at the Centre for the Study of Existential Risk; she holds law degrees from Strathmore University and Columbia Law School.
Developing an innovative wisdom layer for AI that enhances its capabilities for deep analysis, safe AI, and creative solutions to complex systemic problems.
RiesgosIA.org is a Spanish-language non-profit providing open-access tools and educational resources on AI safety and governance, primarily serving Spanish-speaking communities.
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Geoffrey Hinton is a Director of The AI Safety Foundation and a pioneering British-Canadian researcher in artificial intelligence whose work on neural networks helped launch the deep learning revolution. He is Professor Emeritus at the University of Toronto and a 2024 Nobel Laureate in Physics for contributions to the theory and practice of artificial neural networks, and he now devotes much of his work to understanding and communicating the long-term safety risks of advanced AI systems.
UC Berkeley is a leading public research university and one of the world's foremost hubs for AI safety research, hosting CHAI, BAIR, CLTC, and other major centers focused on beneficial and safe AI development.
This grant is funding a $35,000 stipend plus $10,000 in compute costs for Yuxiao Li's independent inference-based AI interpretability research.
Tyler Whitmer is the founder and CEO/President of Legal Advocates for Safe Science and Technology (LASST), a 501(c)(3) nonprofit that uses legal advocacy to make advances in science and technology safer for people and the planet. He previously spent over 15 years as an associate and then partner at Quinn Emanuel Urquhart & Sullivan, LLP as a commercial litigator, and later served as the first general counsel of a 501(c)(3) public charity before leaving that role at the end of 2023 to start LASST.
~4 FTE for 9 months to fund WhiteBox Research, mainly for the 2nd cohort of our AI Interpretability Fellowship in Manila
A private Ivy League research university in Ithaca, New York, with multiple faculty and labs engaged in AI safety, alignment, and responsible AI research, serving as the institutional home and fiscal recipient for SFF-funded work.
3-month funding for upskilling in technical AI Safety to test personal fit and potentially move to a career in alignment
De-risking AI Catastrophe: A cyber-physical protocol using ZKPs and NIR Spectroscopy to resolve the governance deadlock in critical global infrastructure.
Tom David is President of the GPAI Policy Lab, a Campus Cyber–based policy lab that fosters international cooperation on general-purpose AI security and control through research, strategic advising, and training, and he is also co-founder of PRISM Eval, which specializes in stress-testing generative AI models on critical behaviors.
Senior AI Policy Researcher at the Safe AI Forum and Research Affiliate at the Oxford Martin AI Governance Initiative, focusing on identifying areas of possible agreement between leading AI powers and supporting the International Dialogues on AI Safety (IDAIS). Previously a Winter Fellow at the Centre for the Governance of AI in Oxford and a management consultant at Bain & Company in Singapore, he holds a Master’s in Global Affairs from Tsinghua University’s Schwarzman Scholars programme and a bachelor’s in Politics and Anthropology from the University of Cambridge.
Tilman Räuker is Co-Director of Pivotal Research, a fellowship program supporting researchers working on global catastrophic risk reduction with a focus on technical AI safety and AI governance. He holds a Master's degree from Leibniz University Hannover, where his thesis focused on temporally-extended reinforcement learning in dynamic algorithm configuration. His research centers on mechanistic interpretability and understanding the internal representations of deep neural networks, including work on transformer world models. He co-authored the widely-cited survey "Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks" (SaTML 2023), as well as papers on structured world representations and causal world models in maze-solving transformers, published at NeurIPS and ICLR. He previously served as a Technical AI Safety Research Manager at the ERA Fellowship and led requests for proposals on Cybersecurity AI and AI Agent Evaluation at the AI Safety Fund. He also participated in a FAR Labs residency researching goal-directedness in transformer models.
A biosecurity nonprofit working to protect humanity against catastrophic pandemics through AI risk evaluation, pathogen-agnostic early warning surveillance, and DNA synthesis screening.
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Beijing-AISI is a Beijing municipal government-backed research institute dedicated to AI safety evaluations, governance frameworks, and safety standards for large language models and AI systems.
6 months salary to do independent AI alignment research focused on formal alignment and agent foundations
Software engineer on the benchmarking team at Epoch AI, working on infrastructure for running AI benchmarks and improving new benchmarks, with prior experience in data science and cloud infrastructure at BlueCat and embedded GPU technology at Broadcom, and a degree in Mathematics and Computer Science from Oxford.
Preeti Ravindra is a Senior AI Security Researcher at Confidential AI Neocloud and a technical leader who has spent over a decade applying AI to security problems to make AI systems more reliable and trustworthy. Her career spans startups to Fortune 100 companies, where she has advanced research into scalable, revenue‑aligned systems and worked across security operations, detection engineering and vulnerability management while bridging research and engineering execution. She is a recognised industry voice, speaking at conferences such as DEFCON and BSides, serving on program committees for WiCyS and CAMLIS, and helping bridge the AI and security communities while supporting early‑career professionals.
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