Research Scientist, Applied White-Box Methods

The posting, as FAR.AI wrote it

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 50+ 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 including a Best Paper Honorable Mention in 2026 https://icml.cc/virtual/2026/oral/71065, 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, Wired Magazine https://www.wired.com/story/jailbreaking-ai-models-google-anthropic-openai-spacexai/ 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, 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, and publish the AI Security Leaderboard https://leaderboard.far.ai/ based on our red-teaming expertise. 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 THE APPLIED WHITE-BOX METHODS TEAM

The Applied White-Box Methods team develops, evaluates, and demonstrates methods that leverage model internals to improve the safety of AI systems. We work on diverse AI safety applications of white-box methods, from white-box control to evaluation awareness to shaping training dynamics to improve alignment.

Black-box methods, such as chain-of-thought monitoring, work well for now, but we are quickly entering a world where black-box interventions and monitoring are insufficient. Interpretability research is still often early-stage, curiosity-driven work without realistic evaluations on applications that matter. The team bridges the gap between exploration and deployment by stress-testing white-box methods on real-world (e.g. long-context agentic coding) tasks and using this feedback loop to enable the deployment of better white-box methods at frontier scale.

Our scope includes any method
[…]
istry, materials science, robotics, etc.

BENEFITS*

• 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

*(Available only to full-time employees located in the US)

LOGISTICS

• If based in the USA, you will be an employee of FAR.AI http://FAR.AI, a 501(c)(3) research non-profit. Outside the USA, you will be an employee of an EoR organization on behalf of FAR.AI http://FAR.AI.
• Location: Berkeley, CA. This role is in-person; we strongly prefer candidates who are in the Bay Area or willing to relocate, and we sponsor visas for in-person employees. We will consider remote arrangements for exceptional candidates.
• Hours: full-time (40 hours/week).
• Compensation: $150,000 to $250,000 per year. Compensation is determined by our salary bands and your level of experience, with adjustments for location. We also pay for work-related travel and equipment expenses, and offer catered lunch and dinner at our office in Berkeley.
• Application process: interviews with members of our technical staff, followed by a paid work trial of up to one week. If you are not available for a work trial we may be able to find alternative ways of assessing fit.

If you have any questions about the role, feel free to contact us at talent@far.ai. Otherwise, if you don't have questions, the best way to ensure a proper review of your skills and qualifications is by applying directly via the application form. Please don't email us to share your resume (it won't have any impact on our decision). Thank you!