Research Engineer

Research Engineer ABOUT US FAR.AI http://FAR.AI is a non-profit AI research institute dedicated to ensuring 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. Founded in July 2022, we have grown quickly to 30+ staff https://www.far.ai/about/team. We are uniquely positioned to conduct technical research at a scale surpassing academia and leveraging the research freedom of being a non-profit. Our work is published at top conferences (e.g. NeurIPS, ICLR, ICML) and cited by leading media outlets such as 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/. FAR.AI http://FAR.AI uses three prongs working together to improve AI safety: - FAR.Research - we conduct cutting-edge AI safety research https://www.far.ai/research in-house and dispense grants https://www.far.ai/programs/grantmaking to support the wider research community. - FAR.Futures - we bring together key policy makers, researchers and companies to drive change, such as the San Diego Alignment Workshop https://www.far.ai/events/event-list/san-diego-alignment-workshop or the Guaranteed Safe AI research roadmap https://arxiv.org/abs/2405.06624 written with Yoshua Bengio. - FAR.Labs - we host a co-working space in Berkeley to help to incubate other AI safety organizations, currently housing 40 members. 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. We are aiming to rapidly grow our team in the following areas especially, at varying levels of seniority: - Evals and 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; and exploring new threat models (e.g. persuasion https://arxiv.org/abs/2506.02873, tampering risks https://arxiv.org/abs/2507.11630). - Infrastructure: Maintaining GPU compute infrastructure to support experiments with open-weight models and developing new tooling to allow our research teams to scale their fine-tuning and post-training workflows to frontier open-weight models. We are also seeking more senior candidates in the following research areas: - Mitigating AI deception: Studying when lie detectors induce honesty or evasion https://www.far.ai/news/avoiding-ai-deception, and developing for deception and sandbagging - Adversarial Robustness: Working to rigorously solve these 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. FAR.AI http://FAR.AI is one of the largest indepe ... (truncated, view full listing at source)