Research Lead

FAR.AI•Berkeley, CA

2d•Hybrid

About The Position

FAR.AI is seeking a Research Lead to develop and lead a research agenda focused on reducing catastrophic risks from advanced AI. This role involves building and leading a team, setting research direction, mentoring technical staff, and contributing directly to coding and experiments when necessary. The primary goal is to produce research that influences the behavior of AI labs and governments, rather than solely focusing on academic publications. This position is ideal for individuals seeking an impact-driven environment with significant autonomy, pursuing empirically-grounded and scalable Machine Learning safety work.

Requirements

A strong existing research track record in AI or another highly technical subject (e.g., CS, math, physics).
A clear view of which safety research directions are likely to matter most over the next few years, and why.
Either (a) a clear research agenda you'd pursue at FAR.AI, with a theory of change explaining why it's valuable, or (b) a strong track record and a research space you'd sharpen into an agenda over your first months.
Experience leading a team, mentoring graduate students, or supporting early-career researchers through fellowship programs. Informal leadership in flatter organizations counts.
Ability to effectively communicate novel methods and solutions to both technical and non-technical audiences.
Hold a PhD or have 2+ years research experience in computer science, artificial intelligence, machine learning, or statistics.

Nice To Haves

An established publication record in AI safety.
Comfortable writing grant proposals and navigating collaborations with other organizations or external research groups.

Responsibilities

Articulate a research agenda with a clear theory of change for mitigating catastrophic risks from human-level or superhuman AI systems, and/or vastly increasing the upside of such systems.
Grow and lead a team of technical staff in pursuit of this agenda, either directly or in partnership with an engineering co-lead.
Lead novel research projects where there may be unclear markers of progress or success.
Share research findings through written content (e.g., academic publications, blog posts) and presentations (e.g., ML conferences, policymaker briefings) to drive adoption and change.
Mentor and coach junior team members in research skills and ML engineering.
Contribute to the FAR.AI intellectual environment, for example by giving feedback on early-stage proposals.