Tarun Sunkaraneni
Machine Learning Engineer at Fiddler AI
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTarun Sunkaraneni is a Machine Learning Engineer based in San Francisco with 10 years of experience building production-grade backend systems and deploying deep learning models at scale. He currently architects heterogeneous Ray clusters and leads model training, quantization, and deployment work while wrestling with Hugging Face and PyTorch at Fiddler AI. Previously he contributed to Azure's Bicep language and backend services for Azure Resource Manager at Microsoft, improving core parsing, lexer error handling, and template metadata—work that speaks to both systems-level rigor and attention to developer experience. His background spans fintech and fraud detection to large-scale data ingestion and monitoring, and he holds a BS in Computer Science (Cum Laude, with Honors) from the University of Utah and graduate studies at Rice University. Colleagues would describe him as a pragmatic engineer who moves models from research into resilient production environments and surfaces subtle validation and error-reporting improvements that reduce operational friction.
10 years of coding experience
6 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at Rice UniversityThe University of Utah
Stackoverflow
1reputation
0reached
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Github Skills (6)
parserator10
error-handling10
parser10
bicep10
lexer10
arm-templates9
Programming languages (7)
Github contributions (5)
Azure/bicep
Oct 2020 - Nov 2021
Bicep is a declarative language for describing and deploying Azure resources
Role in this project:
Back-end Developer
Back-end DeveloperContributions:387 reviews, 29 commits, 310 PRs in 1 year 1 month
Contributions summary:Tarun contributed to the Bicep project by implementing better error messages for double quotes in the Bicep language and improving error handling for comma separators. They worked on the core parser and lexer, specifically modifying the code to identify and report specific error scenarios. Additionally, the user addressed issues related to readonly properties and runtime property references, enhancing the language's validation logic and error reporting. They also contributed to the template generation metadata.
deployingazure-resourcesarm-jsondeclarativedeclarative-language
Streams-ML/Streams-ML
Jun 2018 - Aug 2022
An interactive way for people to learn and use various streaming algorithms.
Contributions:2 PRs, 18 pushes, 2 branches in 4 years 2 months
machinelearningstreaming-algorithmsstreamingmachine-learning
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