Research Lead

FAR.AI is hiring a Research Lead to develop and lead a research agenda that reduces 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 remaining hands-on enough to write code and run experiments yourself. What counts is whether AI labs and governments actually change how they act; publications are useful but aren't the measure. Beyond your team, you can shape FAR.AI's broader work by directing millions of dollars in grants to external researchers extending your agenda, convening the people who can act on it, and influencing our independent testing and advising of AI companies and governments. This role suits you if you want high autonomy in an impact-driven environment, pursuing empirically grounded, scalable ML safety work.

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, published 40+ academic papers, and convened leading AI safety events. Our work is recognized globally, with publications at premier venues such as NeurIPS, ICML, and ICLR, and features in the Financial Times, Nature News and MIT Technology Review. We conduct pre-deployment testing on behalf of frontier developers such as OpenAI and independent evaluations for governments including the EU AI Office. 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.

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 recent and ongoing 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, 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.

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.

Evals: developing evaluations for new threat models, e.g. persuasion and tampering risks.

Mitigating AI deception: studying when lie detectors induce honesty or evasion, and developing approaches to deception and sandbagging.

We are particularly looking to add Research Leads in the following pod shapes:

• Applied Interpretability — using interpretability to tackle concrete safety problems (better probes, backdoor detection, deception monitoring), aiming for fast feedback loops, often in collaboration with our other pods. A new pod, greenfield.

• Scalable Oversight / Alignment — methods that keep oversight robust as models become more capable than their supervisors: recursive reward modeling, debate, weak-to-strong generalization, process-based supervision.

• Adversarial Robustness —extending our independent-testing work into deployed-system protection: better safety guardrails, pre-training safety interventions (initially CBRN misuse, especially for open-weight models), backdoor detection and mitigation, realistic cybersecurity evaluations, and loss-of-control deception evaluations.

• Auditing / Evals — safety and alignment auditing: evaluation awareness (construct validity, safety-relevance, hyper-realistic evals), CoT monitorability and faithfulness training, black-box monitoring as a complement to our existing white-box work.

• Persuasion / Epistemic Risks — science of epistemic risks and intervention points, persuasion's role in loss of control risks, evaluations and independent testing, connections to broader harmful manipulation, solutions and epistemic uplift. Building on our existing work and shaping your own agenda in the area.

• Bring Your Own Agenda — an open track for senior researchers with a strong vision outside the pods above.

Research Leads define and own a research workstream end-to-end. Day-to-day, that means:

• 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 your 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.

• Build a research field around your agenda through FAR.AI's grantmaking and events, and connect it to real-world deployments through our independent testing and government advising.

This role would be a great fit if you:

• Want to work on the most impactful research directions, alongside mission-driven colleagues who'll push them forward with you.

• Wish to pursue empirically grounded, scalable research directions that lean, technically strong teams can drive forward.

• Value the ability to speak freely. We don't censor our researchers — we just ask that you protect confidential information and make clear when you're speaking personally or on behalf of the organization.

• Want to advise and collaborate with governments, leading AI companies, and academics. We're a small organization that punches above its weight by working closely with these partners — through red-teaming, technical standards work, and research collaborations.

This role would be a poor fit if you:

• Prefer solo IC research to leading a team toward a shared agenda. Some people can do great research that way, but in this role we're looking for someone whose research direction is strong enough that other excellent researchers want to build it with them.

• Prioritize novelty and intellectual elegance over impact. We care about both — a mathematically elegant solution to AI safety would be wonderful — but when we have to choose, we choose what makes AI safer in practice.

• Can only work with the largest compute clusters available at industry labs or need to be compensated with equity in a rapidly growing startup. We offer competitive salaries and sizable compute budgets on a cluster that we manage, but if you value these things over having a positive impact on the future, then you may be more suited to a for-profit lab.

To be a strong candidate for the Research Lead role, you likely:

• Have a strong existing research track record in AI or another highly technical subject (e.g. CS, math, physics).

• Have a clear view of which safety research directions are likely to matter most over the next few years, and why.

• Have 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. We assess both paths against the same bar — depth of articulation at application is itself a signal about expected runway.

• Have led a team, mentored graduate students, or supported early-career researchers through fellowship programs. Informal leadership in flatter organizations counts, as we’re more interested in experience than job titles.

• Can effectively communicate novel methods and solutions to both technical and non-technical audiences.

• Are not a new entrant to AI safety. We don't require a PhD or specific years of experience, but you should have engaged substantively with the field — through prior research, employment, or sustained independent contribution.

It is preferable if you:

• Have an established publication record in AI safety.

• Are comfortable writing grant proposals and navigating collaborations with other organizations or external research groups.

If you are missing key leadership experience or are earlier in your career, we encourage you to consider the open Research Scientist pathway and invite you to contribute to one of our existing agendas.

We're also open to more senior versions of this role; simply apply or reach out to [email protected].

If based in the USA or Singapore, you will be an employee of FAR.AI (501(c)(3) research non-profit / non-profit CLG). Outside the USA or Singapore, you will be employed via an EOR organisation on behalf of FAR.AI or as a contractor.

• 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).

• Compensation: $170,000–$270,000/year depending on experience and location, with the potential for additional compensation for exceptional candidates. We will also pay for work-related travel and equipment expenses. We offer catered lunch and dinner at our offices in Berkeley.

• Application materials: Expect ~1–2 hours of preparation; most carries forward from prior job searches. We ask for a CV, a short research direction statement (the form supports both fully-formed agendas and developing ones), 2–3 selected works with a brief note on your personal contribution, and a short note on why FAR.AI is a good home for your direction. If you advance to portfolio review, we'll ask for a full research direction statement (1–2 pages, with a theory of change to real-world implementation; ~1.5–2 hours, due within about a week).

• Process: From application: a portfolio review (async), a 60-minute bilateral fit call, a research deep-day (~3.5 hours live, including an open talk to FAR research staff and two interview sessions), a 5-day paid work trial, structured reference calls, and a final decision panel. Typical elapsed time: 4–6 weeks. Total candidate time end-to-end is ~50 hours, with the paid work trial being the bulk. If a 5-day block isn't feasible for you, reach out — we can discuss alternatives.

If you have any questions about the role, please do get in touch at [email protected].