Melvin Johnson

One of the central Google researchers to follow for the line from large-scale language modeling into instruction tuning, multilingual systems, and practical model scaling.

One of the more useful people to study for the Gemini era because his work spans both the text-core of multimodal frontier models and the optimization tricks that make those systems cheaper and more stable to train.

A high-signal researcher for understanding the modern scaling playbook, especially around compute-optimal training, retrieval-augmented language models, and the text side of Gemini-era multimodal systems.

A good researcher to follow for the infrastructure side of frontier language models, especially mixture-of-experts scaling, instruction tuning, and the data systems that make very large models usable.

A high-signal reinforcement-learning researcher whose work sits on the path from AlphaGo-era planning systems to Gemini-era reasoning and post-training techniques.

A high-signal researcher for grounded language and retrieval-heavy systems, especially if you want to understand how language models stay useful as the world changes around them.