Researchers

Leads one of the most influential frontier-model labs; key voice on alignment and safe deployment.

Core contributor to modern scaling-law intuition used across frontier training and evaluation.

Bridges frontier labs and public understanding; consistently useful writing on what matters and why.

Works on practical alignment and steering of frontier models; useful lens for post-training tradeoffs.

Prominent alignment researcher focused on making safety work scale with increasingly capable models.

Pushed interpretability forward with tools and approaches that shaped how people reason about neural nets.

Major influence on reward-modeling and oversight ideas that feed into modern post-training.

Central figure in the modern deep-learning wave; shaped large-scale training culture and capability focus.

Key contributor to practical RL algorithms and RLHF-era post-training used in modern assistants.

Drove several foundational generative-pretraining efforts that set patterns for modern foundation models.

Helped establish the modern era of large-scale language modeling and the evaluation mindset around it.

Excellent at translating frontier ideas into practical intuition and tooling for builders.

Worked on instruction-following and RLHF practices that became the standard post-training recipe.

Contributed to post-training workflows and datasets powering instruction-following behavior.

Worked on instruction-following models and post-training practice that influenced the ecosystem.

High-signal work on real failure modes: adversarial examples, extraction, and practical model security.

Built the organization behind many of the last decade’s most visible RL and scientific-AI breakthroughs.

Shaped modern deep RL in practice; a reliable anchor for understanding learning + search.

Leads work that makes large training runs possible and repeatable; crucial but often underappreciated layer.

Strong signal in math/reasoning and verification-style approaches for reliability.

Key figure in sequence modeling and large-scale ML; contributes to frontier model development.

One of the most important builders behind Transformers and MoE scaling that power modern LLMs.

One of the most influential figures in ML systems; shaped the infrastructure that makes frontier training feasible.

Bridges perception and action; useful pick for the robotics + foundation-model convergence.