Working in RLHF means systems level knowledge
Being immersed in frontier model ecosystems—text, vision, audio, and reasoning—has given me a systems-level view of where AI stands today. Working across leading feedback architectures, training GPT, Grok, Meta, with other leading models has shown me first hand how human decisions affect model behavior.
Training data inherits the flaws of human workflows
Nothing moves faster than AI. Defining and refining bottlenecks in RLHF workflows in real time remains an all-world challenge.
Human data flows must evolve continuously so AI’s progress doesn’t just improve—but accelerates by higher magnitudes.
Improving training loops for smarter models
When signal decays, and workflows strain under scale—smarter calibration, clearer alignment, and tighter QA loops show the path forward. Precision emerges from meticulous workflows. As models evolve, so must the systems that teach them.
