Philip Yang
Software Engineer at databricks
San Francisco, California, United States
Summary
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RockstarPhilip Yang is a software engineer based in San Francisco with nine years of hands-on experience building back-end systems and machine learning integrations. He contributes to prominent open-source projects such as Databricks' spark-deep-learning, where he enhanced Scala SQL UDF support and implemented a JVM interface to enable custom and TensorFlow-based UDFs in Apache Spark pipelines. Comfortable across ML engineering and backend development, he writes robust unit tests, leads refactors, and closes critical review feedback to improve production reliability. Philip pairs practical production-first instincts with an appreciation for developer ergonomics, making complex distributed ML tooling easier to adopt.
9 years of coding experience
Github Skills (6)
apache-spark10
tensorflow10
scala10
jvm9
sql9
unit-testing8
Programming languages (4)
Github contributions (5)
databricks/spark-deep-learning
Jun 2017 - Nov 2017
Deep Learning Pipelines for Apache Spark
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:80 commits, 23 PRs, 50 pushes in 5 months
Contributions summary:Philip primarily focused on enhancing the Scala SQL UDF support within the project. Their contributions involved implementing a JVM interface for UDF registration, supporting standard and TensorFlow-based UDFs, and exporting JVM Spark instances. They also addressed code review feedback, added unit tests, and refactored code for improved functionality. This work directly supports the project's goal of providing deep learning pipelines for Apache Spark by enabling custom function integration.
data-sciencedeep-learningmachine-learningapachespark
phi-dbq/spark-deep-learning
Jun 2017 - Nov 2017
Contributions:1 release, 26 PRs, 391 pushes in 5 months
data-sciencedeep-learningmachine-learningapachespark
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