Nathan Brake is a Senior Machine Learning Engineer with eight years of experience building and deploying ML systems, now focusing on NLP and model-driven productivity at Mozilla.ai. He combines a strong engineering foundation—from embedded and electrical systems to cloud-native services—with advanced ML research training (MS, Georgia Tech) to train and evaluate LLMs and specialized architectures for clinical and speech domains. At Solventum he applied LoRA, PEFT, PyTorch DDP and Sagemaker to clinical note generation, and earlier roles at M*Modal and in embedded systems gave him deep production experience in ASR, C++, Go, Kubernetes, and real-time control. He is pragmatic about moving models into reliable, observable pipelines and enjoys exploring how small architectural and tooling choices unlock outsized productivity gains. Based in Pittsburgh, he brings a blend of research rigor and hands-on production craft that helps bridge experimental ML with robust product delivery.
8 years of coding experience
9 years of employment as a software developer
Master of Science - MS, Computer Science, Machine Learning, Master of Science - MS, Computer Science, Machine Learning at Georgia Institute of Technology
Bachelor of Science (BS), Electrical Engineering, Bachelor of Science (BS), Electrical Engineering at Grove City College
Open source platform for the machine learning lifecycle
Contributions:29 pushes, 1 branch in 1 year 10 months
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Nathan Brake - Senior Machine Learning Engineer at Mozilla.ai