Javier Luraschi
Founder & CEO at Hal9
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolJavier Luraschi is a founder and CEO and seasoned software engineer with 13 years building data and ML tooling, currently leading Hal9 to make generative AI accessible across organizations from Seattle. He combines deep open-source craftsmanship—contributions to high-profile projects like dplyr, dbplyr, sparklyr and Apache Arrow—with product leadership that spans R/Python integrations, MLflow and TensorFlow model export. Previously at RStudio and Microsoft Research he drove major R-to-big-data and deep-learning integrations (sparklyr, reticulate, keras/tensorflow bindings) and helped shepherd sparklyr into the Linux Foundation. Javier bridges low-level systems work (C/C++, SQL translation, Arrow/Parquet) with user-facing developer tools and documentation, a mix that lets him turn ambitious ML ideas into production-ready developer experiences. An unusual strength is shipping cross-language bridges and export pipelines that make models and dataflow portable across ecosystems.
13 years of coding experience
14 years of employment as a software developer
Distributed Systems, Distributed Systems at Universidad PanamericanaUniversidad Nacional Autónoma de México (UNAM)
Spanish, German, Italian, English
Github Skills (57)
data-visualizations10
javascript10
visualization10
c-language10
spark10
data-pipelines10
python10
api-design10
r10
testing10
data-manipulation10
databases10
machine-learning10
data-export10
mlflow10
Programming languages (14)
Github contributions (5)
sparklyr/sparklyr
Apr 2016 - Dec 2020
R interface for Apache Spark
Role in this project:
Back-end Developer
Back-end DeveloperContributions:38 releases, 1 review, 4397 commits in 4 years 8 months
Contributions summary:Javier primarily focused on enhancing the functionality of the R interface for Apache Spark (sparklyr). The commits included the addition of tests to validate the import of long precision decimals and integrations with the spark-install package. The user also implemented features to support parameterization of queries. The user worked on the data pipeline aspects of the project, likely in the development of API integrations.
rsparklyrmachine-learningrstatsspark
rstudio/tensorflow
Oct 2017 - Nov 2019
TensorFlow for R
Role in this project:ML Engineer
ML EngineerContributions:79 commits, 24 PRs, 52 pushes in 2 years 1 month
Contributions summary:Javier contributed to the `rstudio/tensorflow` repository by adding and modifying functions related to exporting, viewing, and interacting with TensorFlow models. They implemented functionality for exporting SavedModels from sessions and tensors, including support for different export options and text-based formats. Additionally, they integrated methods for visualizing saved models with TensorBoard and addressed parameter and documentation issues. The user's work focused on enhancing the usability and functionality of the TensorFlow library within the R environment.
machine-learningtensorflow
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