Research Scientist
Far
About Us 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 50+ staff, published 40+ academic papers, and convened leading AI safety events. Our work is recognized globally, with publications at premier venues such as NeurIPS, ICML including a Best Paper Honorable Mention in 2026, and ICLR, and features in the Financial Times, Nature News, Wired Magazine and MIT Technology Review. We conduct pre-deployment testing on behalf of frontier developers such as OpenAI, independent evaluations for governments including the EU AI Office, and publish the AI Security Leaderboard based on our red-teaming expertise. We help steer and grow the AI safety field through developing research roadmaps with renowned researchers such as Yoshua Bengio; running FAR.Labs, an AI safety-focused co-working space in Berkeley housing 40+ members; and supporting the community through targeted grants 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. We are aiming to rapidly grow our team in the following areas especially, at varying levels of seniority:
Since our founding in July 2022, we've grown to 50+ staff, published 40+ academic papers, and convened leading AI safety events. Our work is recognized globally, with publications at premier venues such as NeurIPS, ICML including a Best Paper Honorable Mention in 2026, and ICLR, and features in the Financial Times, Nature News, Wired Magazine and MIT Technology Review. We conduct pre-deployment testing on behalf of frontier developers such as OpenAI, independent evaluations for governments including the EU AI Office, and publish the AI Security Leaderboard based on our red-teaming expertise. We help steer and grow the AI safety field through developing research roadmaps with renowned researchers such as Yoshua Bengio; running FAR.Labs, an AI safety-focused co-working space in Berkeley housing 40+ members; and supporting the community through targeted grants 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. 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, ChatGPT Agent, GPT-5); developing novel attacks to support this work; and exploring new threat models (e.g. persuasion, tampering risks).
- 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.
- Mitigating AI deception : Studying when lie detectors induce honesty or evasion, and developing model organisms 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, to scaling laws for robustness and jailbreaking constitutional classifiers
- Mechanistic Interpretability : Finding issues with Sparse Autoencoders, probing deception using AmongUs, understanding learned planning in SokoBan and interpretable data attribution.
- New and under-explored AI alignment idea(s).
- Experience leading and/or playing a senior role in research projects related to machine learning.
- Ability to effectively communicate novel methods and solutions to both technical and non-technical audiences.
- PhD or several years research experience in computer science, artificial intelligence, machine learning or statistics.
- Health Insurance - 94% of Insurance premium paid by Organization commencing within 1 month after your start date
- Retirement - 401(k) plan with up to 2% match
- PTO - 25 days Paid Time Off per year, accrued weekly and up to 10 days of paid sick leave per year
- Paid Leave - Paid Bereavement, Family, Medical and Pregnancy Disability Leave
- WFH Stipend & Equipment- Work computer and stipend provided for eligible employees
- Catered Meals (Berkeley Office Only) - Catered lunches and dinners on workdays at our office
- Location: Both remote and in-person (Berkeley, CA or Singapore) are possible. We sponsor visas for in-person employees, and can hire remotely in most countries.
- Hours: Full-time (40 hours/week).
- Application process: A 72-minute programming assessment, two interviews with members of our technical staff, and a paid work trial lasting up to 1 week. If you are not available for a work trial we may be able to find alternative ways of testing your fit.
Vacancy posted 3 days ago
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