Michael Klachko is a Principal ML Engineer with 11 years of experience building and deploying deep learning systems across computer vision, speech, music generation, and large language/diffusion models. He combines a PhD in mixed-signal, ultra-low-power hardware accelerators with hands-on expertise in model compression (quantization, pruning, matrix factorization) and squeezing accuracy/performance under chip area, power and noise constraints. At EnCharge AI he designs quantization algorithms and maintains QAT/PTQ infrastructure and a model zoo for hardware deployment, after driving hardware-aware benchmarking and co-design at Luminous Computing and novel quantization patents at Mythic. Equally comfortable in TensorFlow and PyTorch, he is transitioning to JAX while applying research-grade methods to production accelerators. Based in Santa Barbara, he brings a rare blend of systems-level hardware insight and production ML tooling that accelerates model delivery on constrained silicon.
11 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Engineering, Doctor of Philosophy (Ph.D.), Computer Engineering at University of California, Santa Barbara
Code for "Improving Noise Tolerance of Mixed-Signal Neural Networks" https://arxiv.org/abs/1904.01705
Contributions:108 commits, 1 PR, 104 pushes in 2 years 10 months
pytorchsignalmixed-signalarxivabs
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Michael Klachko - Principal ML Engineer at EnCharge AI