Joseph Bradley
Principal ML Product Specialist at Databricks
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJoseph Bradley is a Principal ML Product Specialist at Databricks with 15 years of experience building production-grade machine learning and graph analytics on Apache Spark. He blends deep academic training (PhD in Machine Learning from Carnegie Mellon) with hands-on engineering—contributing to core Spark and GraphFrames code, ML algorithms (decision trees, random forests, GBTs), and scikit-learn–Spark interop. At Databricks he has moved from engineering into customer-facing and product roles, helping startups, large tech customers, and researchers apply ML at scale. He is comfortable across research, product, and implementation, often translating algorithmic advances into practical tests and integration work. An engineer who still digs into unit tests, API refinements, and algorithmic edge cases, he brings uncommon depth in both graph algorithms and large-scale ML pipelines.
15 years of coding experience
10 years of employment as a software developer
Ph.D. Machine Learning, Ph.D. Machine Learning at Carnegie Mellon UniversityBSE Computer Science, BSE Computer Science at Princeton University
Github Skills (32)
algorithm10
algorithms10
apache-spark10
python10
classification10
scikit10
dataframes10
graph10
big-data10
machine-learning10
data-structure10
ml10
gradient-boosting10
scala10
mle10
Programming languages (8)
Github contributions (5)
databricks/spark-sql-perf
Jun 2016 - Jul 2018
Role in this project:
ML Engineer
ML EngineerContributions:12 commits, 27 PRs, 20 pushes in 2 years
Contributions summary:Joseph primarily contributed to the implementation of machine learning tests within the repository. They added tests for decision tree, forest, and gradient boosting tree (GBT) classification models. The code changes involve modifying existing model building classes and adding new data generation capabilities that specifically support the new tests and their evaluation.
graphframes/graphframes
Oct 2015 - Aug 2018
GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs
Role in this project:Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:3 releases, 113 commits, 67 PRs in 2 years 10 months
Contributions summary:Joseph merged and refactored GraphFrame code related to motif finding, including integrating it into the DFGraph class. This involved significant code modifications within the core data processing component. The user also updated the toGraphX method to handle different ID types and corrected attribute handling within toGraphX. Furthermore, the user introduced new unit tests and implemented a Breadth-First Search (BFS) algorithm using a builder pattern within GraphFrames, demonstrating skills in graph algorithms and DataFrame manipulation.
apache-sparkbig-dataconnected-componentsdataframedataframes
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