Research Lead - Pre-training Safety

FAR.AI is hiring a Research Lead to develop and lead our work on pre-training safety
, shaping models' capabilities and internal representations at their source, rather than trying to fix them after the fact.

Our initial focus is capability control: removing harmful capabilities while preserving benign ones. We see this as a promising way to prevent misuse of open-weight models in areas such as CBRN and cyber by removing offensive capabilities, and reducing loss-of-control risks by removing knowledge of oversight mechanisms. We will validate approaches like pre-training data filtering at scale, drive adoption of successful methods, and explore techniques such as gradient routing and unlearning..

We are scaling methods like ++Deep Ignorance++ by over an order of magnitude (>100B parameter models with >1T tokens). You will direct this work, partner with our red team to stress-test the resulting models, and analyze how well the methods scale to frontier systems.

Our research directions include:

• Improved data filtering methods, such as using data attribution (e.g. influence-based selection) or more sophisticated classifiers

• Using methods like gradient routing to isolate dual-use capabilities in components of the model (e.g. specific MoE experts)

• Training to actively remove harmful capabilities, such as interleaving next-token prediction with unlearning, as opposed to simply filtering data

• Adding synthetic data to pre-training or mid-training to shape the representations and behavior of the model

You’ll build and lead the team, set its research direction, mentor Members of Technical Staff to scale your vision, and remain hand-on enough to write code and run experiments yourself. This role offers high autonomy in an impact-driven environment, pursuing empirically grounded, scalable ML safety research.

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.

We’re structured to support that work from early research through real-world adoption:

Independent by design. We can pursue what’s most impactful based on our theory of change and share what we find publicly.

A portfolio approach. Rather than focus on one single direction, we run diverse bets across the safety stack. We take promising ideas from initial experiments to deployment, informed by red-team partnerships with frontier labs and governments.

Serious infrastructure for ambitious research
. A dedicated engineering team runs our compute cluster and experiment-scaling stack, so researchers spend their time on research instead of on infra.

Setting the standard
. Our events convene key decision makers; our red-team works with frontier developers and governments; and our communications inform the public. Together, this drives adoption and sets the new standard in safety.

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 and independent evaluations for governments ++including the EU AI Office++ and publish the ++AI Security Leader board++ 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…