Summary
Jamie Gainer is a Machine Learning Engineer II with eight years of experience applying rigorous, research-grade techniques to production-scale problems in Ads AI, identity graphs, and scientific discovery. With a PhD in physics and a background in particle physics, Jamie bridges theoretical methods and practical engineering—translating novel ML approaches into deployed models that improved conversion metrics and sped up retrain pipelines by up to 3x. At LinkedIn and Drawbridge they led feature standardization and weak-supervision efforts to scale identity and conversion modeling to hundreds of millions of users. Jamie is comfortable across the stack from algorithm design and graph methods to production Scala and Python code, and has a track record of proposing high-impact efficiency gains such as training 60–70% fewer pairs in graph algorithms. Based in Sunnyvale, they bring a data-first curiosity shaped by both academia and industry, plus an appetite for turning complex research ideas into reliable, repeatable systems.
8 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy - PhD, Physics, Doctor of Philosophy - PhD, Physics at Stanford University
High School Diploma, High School Diploma at Charlottesville High School
AB (Bachelor's), Physics, AB (Bachelor's), Physics at Princeton University