Research Lead

Research Lead FAR.AI http://FAR.AI is seeking a Research Lead to develop and lead a research agenda to reduce catastrophic risks from advanced AI. You'll build and lead a team executing this agenda — setting research direction, mentoring Members of Technical Staff to scale your vision, and staying close enough to the work to write code and run experiments yourself when it matters. The aim is research that changes how AI labs and governments behave, not just research that gets published. This role is a strong fit if you want to work in an impact-driven environment with high autonomy, pursuing empirically-grounded, scalable ML safety work. ABOUT US FAR.AI http://FAR.AI is a non-profit AI research institute working to ensure advanced AI is safe and beneficial for everyone. Our mission is to facilitate breakthrough AI safety research, advance global understanding of AI risks and solutions, and foster a coordinated global response. Since our founding in July 2022, we've grown to 40+ staff https://www.far.ai/about/team, published 40+ academic papers https://scholar.google.com/citations?user=FVJ24k8AAAAJ, and convened leading AI safety events https://far.ai/events/. Our work is recognized globally, with publications at premier venues such as NeurIPS, ICML, and ICLR, and features in the Financial Times https://www.ft.com/content/175e5314-a7f7-4741-a786-273219f433a1, Nature News https://www.nature.com/articles/d41586-024-02218-7 and MIT Technology Review https://www.technologyreview.com/2020/02/28/905615/reinforcement-learning-adversarial-attack-gaming-ai-deepmind-alphazero-selfdriving-cars/. We conduct pre-deployment testing on behalf of frontier developers such as OpenAI and independent evaluations for governments including the EU AI Office https://www.far.ai/news/far-ai-selected-to-lead-eu-ai-act-cbrn-risk-consortium. We help steer and grow the AI safety field through developing https://arxiv.org/abs/2405.06624 research https://arxiv.org/abs/2506.20702 roadmaps https://www.researchgate.net/publication/396910034_Open_Technical_Problems_in_Open-Weight_AI_Model_Risk_Management with renowned researchers such as Yoshua Bengio; running FAR.Labs https://www.far.ai/programs/far-labs, an AI safety-focused co-working space in Berkeley housing 40 members; and supporting the community through targeted grants https://www.far.ai/programs/grantmaking to technical researchers. ABOUT FAR.RESEARCH We explore promising research directions in AI safety and scale up only those showing a high potential for impact. Once the core research problems are solved, we work to scale them to a minimum viable prototype, demonstrating their validity to AI companies and governments to drive adoption. Our current research includes: Adversarial Robustness: working to rigorously solve security problems through building a science of security and robustness for AI, from demonstrating superhuman systems can be vulnerable https://far.ai/post/2023-07-superhuman-go-ais/, to scaling laws for robustness https://www.far.ai/news/does-robustness-improve-with-scale and jailbreaking constitutional classifiers https://arxiv.org/abs/2506.24068. Mechanistic Interpretability: finding https://arxiv.org/abs/2502.12892 issues https://arxiv.org/abs/2508.16560 with https://arxiv.org/abs/2505.11756 Sparse Autoencoders, probing deception using AmongUs https://arxiv.org/abs/2504.04072, understanding learned planning https://far.ai/post/2024-07-learned-planners/ in SokoBan, and interpretable data attribution. Red-teaming: conducting pre- and post-release adversarial evaluations of frontier models (e.g. Claude 4 Opus https://x.com/ARGleave/status/1926138376509440433, ChatGPT Agent https://cdn.openai.com/pdf/839e66fc-602c-48bf-81d3-b21eacc3459d/chatgpt_agent_system_card.pdf, GPT-5 https://cdn.openai.com/gpt-5-system-card.pdf); developing novel attacks https://www.far.ai/news/defense-in-depth to support this work. Evals: developing evaluations for new threat models, e.g. persuasion https ... (truncated, view full listing at source)