Adam Pocock
Principal Member Of Technical Staff, Oracle Labs
Burlington, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolAdam Pocock is a Principal Member of Technical Staff at Oracle Labs with 14 years of experience building production-ready machine learning systems and developer tooling. He holds a PhD in Machine Learning and leads development on Tribuo while maintaining the Java API for the high-performance ONNX Runtime, demonstrating a rare blend of research rigor and production engineering. His contributions span Java and Scala ecosystems—from TensorFlow and ONNX Runtime Java bindings to maintenance of FACTORIE—focusing on model export, serialization, data ingestion, and robust multi-threaded native integrations. Based in Burlington, MA, he repeatedly bridges ML research and engineering, turning academic ideas into deployable libraries and addressing subtle lifecycle and performance bugs that surface at scale. Colleagues rely on him for deep technical ownership across inference stacks and for making complex tooling practical for enterprise use.
15 years of coding experience
BSc, Computer Science and Mathematics, 2:1, BSc, Computer Science and Mathematics, 2:1 at The University of Manchester
Stackoverflow
26reputation
908reached
1answer
0questions
Github Skills (31)
lib10
tensorrt10
machine-learning10
code-library10
feature-engineering10
data-structure10
java10
scala10
javas10
tensorflow10
onnxruntime10
data-structures10
refactoring10
serializer9
operation9
Programming languages (11)
Github contributions (5)
oracle/tribuo
Jun 2020 - Jan 2023
Tribuo - A Java machine learning library
Role in this project:
Backend Developer & ML Engineer
Backend Developer & ML EngineerContributions:12 releases, 300 reviews, 296 commits in 2 years 7 months
Contributions summary:Adam's contributions focused on enhancing the columnar package within the Tribuo machine learning library. The commits involved refactoring code within the columnar package, including exception handling and feature processor improvements, alongside the addition of the UniqueProcessor to handle feature stream uniqueness. They also made substantial changes to the examples and data loading functionality, showing a focus on improving the data ingestion process for machine learning workflows. Furthermore, the user implemented and integrated several machine learning model export and serialization features, reflecting their proficiency in building and maintaining ML systems.
javamachine-learningdeep-learningclassificationregression
tensorflow/java
Nov 2019 - Nov 2022
Java bindings for TensorFlow
Role in this project:Back-end Developer
Back-end DeveloperContributions:298 reviews, 39 commits, 57 PRs in 3 years 1 month
Contributions summary:Adam primarily worked on refactoring and updating the Java bindings for TensorFlow. They focused on making the ops generation deterministic by enforcing a specific ordering. The contributions included code changes in `ImageOps.java` and refactoring to uptake the latest changes in `tensorflow-core`. The user also addressed bug fixes in `MNISTTest.java`.
java-bindingstensorflow
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