I’m a low-level, math-driven systems engineer with a PhD in Machine Learning and 7 years of production C++ experience at Apple. I love the challenge of making models run fast and efficiently on real hardware and I’ve shipped optimized ML features across multiple platforms. Earlier this year I shifted my full attention to model inference, combining my ML background with years of shipping performant systems at scale. I’ve been deep in GPU optimization ever since.
Prior to Apple, I worked on vision, tracking, and generative AI problems, implementing papers and writing training pipelines (some trace of that era is on my Github). My PhD was on unsupervised and active learning, but included some reinforcement and deep learning.
Seeking opportunities!
I’m now actively seeking roles in SF or remote, preferably in the domain of AI/ML. Please reach out via email.