Andrew Audibert
Software Engineer at Google
California, United States
Summary
🤩
Rockstar🎓
Top SchoolAndrew Audibert is a software engineer with 11 years of experience building and optimizing large-scale data systems, currently at Google in California. He has a strong backend and performance focus demonstrated by contributions to TensorFlow (tf.data performance, checkpointing, and docs) and to Alluxio’s core client and state management. His background includes work on data transformation DSLs at Palantir and search infrastructure at Twitter, giving him deep familiarity with distributed systems and developer tooling. Andrew blends hands-on engineering with technical writing—he has improved TensorFlow docs to align examples with style guides and clarified complex dataset behaviors. He is comfortable across languages and environments, from JVM-based systems to machine learning infrastructure, and often focuses on making flaky tests and memory issues disappear. Collected teaching and early research experience from Carnegie Mellon suggest a methodical, pedagogy-minded approach to complex engineering problems.
11 years of coding experience
5 years of employment as a software developer
Bachelor of Science (BS), Computer Science, Senior, Bachelor of Science (BS), Computer Science, Senior at Carnegie Mellon University
Github Skills (22)
filesystem10
performance-analytics10
performance-monitor10
python10
machine-learning10
java10
performance-measurement10
javas10
performance-analysis10
deep-learning10
tensorflow10
journaling10
performance-tuning10
performance-monitoring10
documentation10
Programming languages (10)
Github contributions (5)
Alluxio/alluxio
Sep 2015 - May 2019
Alluxio, data orchestration for analytics and machine learning in the cloud
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 releases, 3005 commits, 2263 PRs in 3 years 8 months
Contributions summary:Andrew made a series of code changes aimed at fixing tests, primarily within the Alluxio client. These changes involved modifications to files related to file streams, block operations, and system tests, suggesting a focus on improving the robustness and reliability of core Alluxio functionalities. Furthermore, the user also addressed issues related to journal entries and the overall state management.
data-analysisvirtual-distributed-filesystemanalyticsorchestrationdata-science
tensorflow/tensorflow
Jun 2019 - Jan 2023
An Open Source Machine Learning Framework for Everyone
Role in this project:Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:101 reviews, 448 commits, 5 PRs in 3 years 7 months
Contributions summary:Andrew primarily focused on optimizing the performance and addressing issues related to tf-data in the TensorFlow project. Their contributions included converting error statuses, disabling flaky tests, and optimizing byte counting code. Additionally, the user improved test naming, fixed memory issues, and added support for checkpointing datasets, demonstrating expertise in data processing and performance tuning within the TensorFlow framework.
pythondata-sciencedeep-learningmlmachine-learning
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