Summary
Mehrdad Yazdani is a Machine Learning Engineer with 12 years of experience applying research-driven methods to production ML problems, most recently leading modeling, evaluation, and tooling across WhatsApp integrity, GenAI, and recommendation at Meta. He specializes in preference learning, reward modeling, off-policy evaluation, retrieval, and optimization, and enjoys designing precise objectives, thoughtful experiments, and diagnosing failure modes that generalize beyond single products. His background spans academic research—from a PhD in Intelligent Systems to postdoctoral work in computational neuroscience—to industry roles improving real-world systems and developer workflows. Notably, he blends deep theoretical grounding (sparse optimal control, convex optimization) with hands-on system improvements like architecture exploration, hyperparameter and data-quality analysis, and cross-functional launch readiness. Based in Los Angeles, he gravitates toward problems where careful evaluation and objective design unlock robust, scalable ML solutions.
12 years of coding experience
12 years of employment as a software developer
University of California San Diego
English, Persian