Jobs in Berkeley, CA

About the role

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. 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. You'll build and lead the team, set its research direction, mentor Members of Technical Staff to scale your vision, and remain hands-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. Research Leads define and own a research workstream end-to-end. Day-to-day, that means: 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.

What you'll bring

• To be a strong candidate for the Research Lead - Pre-Training Safety role, you likely:
• Have a strong existing research track record in AI or another highly technical subject (e.g. CS, math, physics).
• Deep experience with language-model pretraining, dataset construction, or controlled training experiments.
• Experience building large-scale pipelines for scoring, filtering, deduplicating, and sampling training corpora.
• Strong experimental judgment, including safety–capability evaluations, distribution-shift analysis, and statistically rigorous model comparisons.
• Ability to build and debug research systems directly, from classifier fine-tuning through distributed training and evaluation.
• 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 machine learning research.

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