Arjun Gopalan
Staff Software Engineer at Google
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolArjun Gopalan is a Staff Software Engineer in Mountain View with six years of professional experience building scalable distributed systems and production ML pipelines. At Google he focuses on research and machine intelligence, complementing earlier roles where he architected high-throughput RPC pipelines, synchronous replication, and integrity checkers for storage systems. His academic work at Stanford on RAMCloud and scalable secondary indexes resulted in multiple publications, and he helped drive systems research into production. On GitHub he contributes to TensorFlow projects—improving documentation, adding a graph-building library, and integrating Neural Structured Learning into TFX with Apache Beam for scalable preprocessing. He blends deep systems engineering with practical ML pipeline work, and is comfortable moving between low-level protocol design and high-level model deployment. A less obvious strength is his track record of turning research prototypes into revenue-impacting features for large customers.
7 years of coding experience
5 years of employment as a software developer
BITS Pilani, Birla Institute of Technology and ScienceMS, Computer Science, MS, Computer Science at Stanford University
Github Skills (12)
neural-network10
tfx10
keras10
machine-learning10
tensorflow10
data-pipelines10
data-pipeline10
apache-beam10
python10
pipe10
pipeline10
documentation10
Programming languages (2)
Github contributions (4)
tensorflow/neural-structured-learning
Sep 2019 - Sep 2022
Training neural models with structured signals.
Role in this project:
Back-end Developer & Technical Writer
Back-end Developer & Technical WriterContributions:1 release, 21 reviews, 68 commits in 3 years 1 month
Contributions summary:Arjun primarily focused on improving the documentation and tutorials within the repository. Their contributions involved updating the description of the NSL pip package, fixing typos in the IMDB tutorial, and fixing documentation inconsistencies. They also added a library for graph building, reflecting a contribution to core functionality, and updated tutorial notebooks to use library APIs for graph building.
adversarial-learningautoencodergraph-learningstructureddeep-learning
tensorflow/tfx
Aug 2020 - Apr 2021
TFX is an end-to-end platform for deploying production ML pipelines
Role in this project:ML Engineer
ML EngineerContributions:11 commits in 8 months
Contributions summary:Arjun contributed to the development of the NSL TFX tutorial, primarily focusing on integrating Neural Structured Learning techniques within the TFX framework. Their work involved replacing Keras layers with legacy TensorFlow layers, cleaning up imports, and updating the tutorial to use the latest TFX version. They also implemented Apache Beam for parallel processing to assign unique IDs to examples, improving scalability.
machine-learningtfxtensorflowapache-beam
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