Ryan Dailey is an AI research scientist and principal data scientist with a decade of experience building scalable ML systems, from large-scale data collection and web scraping to edge deployment and model quantization. He has led architecture and engineering efforts at Capital One and Latent AI, designing monitoring pipelines and an orchestration tool that managed millions of deep learning experiments across heterogeneous hardware. Ryan bridges research and production—shipping scikit-learn style packages, Plotly-based monitoring UIs, and cross-platform Cython/Python bindings to simplify complex multi-platform runtime and compiler dependencies. His work on templated deployment examples and automated calibration for quantized models has reduced integration friction for edge customers and removed reliance on cumbersome toolchains. Notably, he combines hands-on low-level systems work with product-facing collaboration, securing stakeholder buy-in to turn prototypes into enterprise-ready solutions. Based in McLean, VA, he brings an unusual mix of data-centric research, distributed systems, and deployment engineering informed by a M.Eng. in large-scale data collection and computer vision.
9 years of coding experience
6 years of employment as a software developer
Master of Engineering - MEng Large Scale Data Collection Data Sicence and Computer Vision, Master of Engineering - MEng Large Scale Data Collection Data Sicence and Computer Vision at Purdue University
Contributions:97 commits, 1 PR, 16 pushes in 1 year 8 months
apidatabase-apicamerapurduedatabase
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Ryan Dailey - AI Research Scientist II at Latent AI