Gerard Saez
L6 Machine Learning Engineer at Block
Houston, Texas, United States
Summary
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Rockstar🎓
Top SchoolGerard Saez is an L6 Machine Learning Engineer with 11 years of experience building production ML platforms and pipelines, currently based in Houston and working at Block. He has deep MLOps and full-stack pipeline expertise, having driven adoption and integration of TFX and Kubeflow Pipelines across teams at Twitter and contributed runtime parameter and orchestration improvements to the upstream TFX Airflow runner. Gerard blends backend SDK work and frontend UX improvements—adding container builder flexibility and pipeline versioning—to make ML tooling more usable for engineers. He has led platform initiatives like internal Slurm clusters, CI/CD systems, and GPU training integrations, and coached teams on scaling ML on GCP. A pragmatic engineer who also mentors and runs developer tooling, he pairs hands-on coding (and a well-known love of coffee) with cross-team technical leadership. His background spans research-grade big data systems to enterprise ML productionization, reflecting both academic rigor and delivery focus.
11 years of coding experience
8 years of employment as a software developer
Baccalaureate Tecnology, Baccalaureate Tecnology at Santa Teresa de LisieuxMaster's degree Computer Science, Master's degree Computer Science at University of Colorado BoulderUPC Universitat Politècnica de Catalunya
Catalan, Spanish, English
Stackoverflow
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Github Skills (21)
kubernetes10
data-pipelines10
python10
kubeflow10
machine-learning10
mlops10
sdk10
front-end-development10
airflow10
pipe10
kubernetes-pods10
pipeline10
frontend10
data-pipeline10
react10
Programming languages (16)
Github contributions (5)
tensorflow/tfx
May 2020 - Feb 2022
TFX is an end-to-end platform for deploying production ML pipelines
Role in this project:
MLOps Engineer
MLOps EngineerContributions:19 reviews, 57 commits, 17 PRs in 1 year 9 months
Contributions summary:Gerard's contributions focused on enhancing the TFX orchestration capabilities within the repository. They implemented and tested features related to runtime parameters in the Airflow DAG runner, enabling dynamic configuration. The changes include modifications to the Airflow component and improvements to the test suite for the DAG runner, demonstrating a focus on improving the integration and testing of TFX pipelines on Airflow. Furthermore, the user addressed comments and issues, refining the runtime parameter functionality.
deployingend-to-endml-pipelinesmlmlops
kubeflow/pipelines
Jul 2021 - Jan 2022
Machine Learning Pipelines for Kubeflow
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:17 reviews, 10 commits, 10 PRs in 6 months
Contributions summary:Gerard contributed to both the backend and frontend components of the Kubeflow Pipelines project. They enhanced the Python SDK by adding features to the container builder, allowing for more flexible configuration and integration with Kubernetes. Furthermore, the user worked on the frontend, adding features such as pipeline version descriptions and improving the UI. This included API changes and related front-end implementations, demonstrating a full-stack focus.
pipelinetektondata-sciencemachine-learningmlops
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