Arian Arfaian
Staff Software Engineer at Google
Redwood City, California, United States
Summary
🤩
Rockstar🎓
Top SchoolArian Arfaian is a Staff Software Engineer at Google with 12 years of experience building scalable systems and on-device machine learning solutions from Redwood City. He has advanced TensorFlow/TFLite internals—fixing MLIR bugs, improving quantization and sparsity, and adding StableHLO custom call support—helping a flagship open-source framework perform better across platforms. At Google he progressed from Software Engineer to Staff, applying deep systems and ML knowledge to production mobile and edge workflows. Earlier roles include co-founding Revmetrix, where he redesigned SDKs and cut large-scale OLAP query times by over 80%, and architecting event-logging and A/B frameworks at Everfi. He pairs an MS in Systems Engineering with a pragmatic engineering style that bridges low-level optimization and product-facing reliability. Colleagues would call him a pragmatic problem-solver who surfaces subtle ML/dataflow issues before they reach production.
12 years of coding experience
12 years of employment as a software developer
Master of Science (M.S.) Systems Engineering, Master of Science (M.S.) Systems Engineering at The George Washington UniversityBachelor of Arts (B.A.) Economics, Bachelor of Arts (B.A.) Economics at Vanderbilt University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
quantization10
tflite10
machine-learning10
tensorflow10
stable10
python10
mlr10
build-system9
c-language8
cprogramming-language8
Programming languages (5)
Github contributions (5)
tensorflow/tensorflow
Nov 2022 - Jan 2023
An Open Source Machine Learning Framework for Everyone
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 9 commits, 4 comments in 2 months
Contributions summary:Arian primarily contributed to the TensorFlow framework by fixing bugs and optimizing code within the MLIR (Multi-Level Intermediate Representation) component. Their work involved addressing issues related to dynamic tensor sizes, integer narrowing, and quantization of the outputs of various operations, especially within the context of the TFLite converter. Furthermore, the user implemented changes to support StableHLO custom call and improved model sparsity and the debug instrumentation in the converter. The user has also worked to improve the build process and added support for additional platforms.
machine-learningtensorflowpythondeep-learningdeep-neural-networks
scache/scache
Apr 2014 - Oct 2014
Contributions:17 commits in 6 months
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