David Soto is a Solutions Architect in Machine Learning with 11 years of experience blending deep technical ML expertise and telecom systems knowledge, now driving large-scale distributed training at Google. He has a strong track record building and optimizing deep learning pipelines, custom LLM training, and edge inference (TensorRT/TF-TRT) across roles from principal engineer to head of ML. His background in electrical engineering, an MBA, and early career in network planning and datacom gives him a rare mix of systems-level networking insight and product/market fluency for service providers. Comfortable across cloud platforms (GCP/Azure/AWS), Python/C++, and production ML workflows, he excels at turning research models into scalable, operational solutions. Notably, he brings hands-on experience optimizing real-time computer vision and LSTM models for embedded and IoT deployments, bridging edge constraints with cloud-scale training.
11 years of coding experience
18 years of employment as a software developer
Data Science, Data Science at Galvanize Inc
Ingeniero Civil Electrónico Electrical and Electronics Engineering, Ingeniero Civil Electrónico Electrical and Electronics Engineering at Universidad de Concepción
Master of Business Administration (MBA) Business Administration and Management General, Master of Business Administration (MBA) Business Administration and Management General at Universidad Adolfo Ibáñez
Diplomado Administración y Dirección de Proyectos, Diplomado Administración y Dirección de Proyectos at Pontificia Universidad Católica de Chile
Contributions:9 PRs, 16 pushes, 4 branches in 5 years 9 months
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David Soto - Solutions Architect Machine Learning at Google