Nick Karpov is a Staff Developer Relations engineer at Databricks with 11 years of experience building and explaining modern data and AI platforms. He combines hands-on application and data engineering—spanning web/mobile front ends, backend APIs, ETL, view maintenance, and ML model training/deployment for stream and batch scoring—with a focus on reusable demo environments and simulation systems for developer education. An active contributor to Apache Spark and Delta Lake ecosystems and to notable open-source ML projects like H2O-3, he has fixed core model issues and implemented low-level functionality such as string operations and session management. Based in Los Angeles, he translates complex infrastructure behavior into technical deep dives, talks, and reproducible demos, and experiments with harness engineering for LLM-based systems and AI agents interacting with real data. Collected across roles from customer success to staff-level developer relations, his background blends production engineering, developer advocacy, and practical open-source contributions.
10 years of coding experience
4 years of employment as a software developer
Bachelor of Science (B.S.), Computer Engineering and Computer Science, Bachelor of Science (B.S.), Computer Engineering and Computer Science at University of Southern California
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Role in this project:
Back-end Developer & ML Engineer
Contributions:41 commits, 13 PRs, 38 pushes in 1 year 2 months
Contributions summary:Nick primarily contributed to the back-end functionality of the H2O-3 platform, focusing on string manipulation operations and model-related functionalities. The user implemented `lstrip` and `rstrip` string functions within the Java-based core and the R package tests. Additionally, the user addressed critical model-related issues, including fixing run time calculation and validating parameters within the GLM model, as well as adding session management to avoid temporary key collisions. The commits also involved documentation updates and a small test case.
An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs for Scala, Java, Rust, Ruby, and Python.
Contributions:27 pushes, 3 branches in 1 year 3 months
prestodbpythonflinkstorageruby
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