Summary
Sam Kennerly is a Machine Learning Engineer with 10 years of experience building full-pipeline data systems across startups, quant finance, and scientific research. He combines a PhD-level physics background and options-quant experience to design robust ETL, ML, and infrastructure-as-code solutions on AWS and GCP using Python, Docker, Pulumi/Terraform, and columnar data formats like Parquet and KDB+. Sam has a track record of productionizing custom models and radically improving data throughput and cost-efficiency—e.g., replacing a costly MongoDB workflow with partitioned column-oriented datasets and delivering a 100x query scaleup at minimal cost. He’s comfortable from sparse-matrix numerical algorithms and graph layouts to secure API integrations and Cloud Run deployments, and he publishes tooling and projects publicly (see his portfolio). Pragmatic and detail-oriented, he excels at automating repetitive tasks and shoring up data quality in messy real-world sources. Based in Chengdu, he brings research rigor plus startup speed to bridge models, pipelines, and operational reliability.
10 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (PhD) Master of Science (MS) Physics, Doctor of Philosophy (PhD) Master of Science (MS) Physics at Drexel University
Theoretical Physics, Theoretical Physics at University of Amsterdam
Bachelor's degree Mathematics, Bachelor's degree Mathematics at Northwestern University
Dutch, German, English, Chinese