5 sources
One of the clearest people to follow if you care about scaling laws, training efficiency, and the systems choices that quietly shape frontier-model progress.
Researcher Profile
Editor reviewedJeffrey Ladish
Alignment via AI feedback (Constitutional AI)
Director of Palisade Research and former Anthropic security researcher
A strong person to know for the security-first side of AI risk work, especially where practical model behavior, jailbreak removal, and broader catastrophic-risk framing start to overlap.
Organizations
Palisade ResearchCenter for AI PolicyAnthropic
Labs
About This Page
This profile is meant to help you get oriented quickly: why this researcher matters, what to read first, and where to explore next.
Last reviewed
March 20, 2026
Known For
The ideas, systems, and research directions that make this person worth knowing.
01
AI security and catastrophic-risk work
02
Safety fine-tuning removal and backdoors
03
Security-minded critiques of model alignment
04
Alignment via AI feedback (Constitutional AI)
05
Constitutional AI: Harmlessness from AI Feedback
06
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
Start Here
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Signature Works
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Supporting Sources
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Related Researchers
People worth exploring next because they share topics, labs, or source material with this profile.
4 sources
Important for understanding how Anthropic’s assistant-training stack evolved from early RLHF into Constitutional AI and later robustness work around jailbreaks and behavior control.
4 sources
Useful for the attack-and-evaluation side of alignment work, especially long-context jailbreak research and the measurement work that turns safety concerns into concrete tests.
Start HereMany-shot Jailbreaking
5 sources
A high-signal person to follow for the part of alignment research that asks whether a model’s stated reasoning can actually be trusted and measured.
5 sources
A strong person to follow for the point where machine learning research starts shaping the compute stack itself, especially in chip placement and systems-aware optimization.
5 sources
High-signal for the seam between machine learning and hardware systems, especially where learned optimization methods begin affecting the actual compute infrastructure underneath frontier models.