Sharath TS
Senior Deep Learning Algorithms Engineer at NVIDIA
California, United States
Summary
🤩
Rockstar🎓
Top SchoolSharath TS is a Senior Deep Learning Algorithms Engineer at NVIDIA with 11 years of experience building and optimizing state-of-the-art neural networks across CV, NLP and RL. He holds a Master's in Computer Science (AI) from UC Santa Cruz and blends research-grade language-generation work with production-focused MLOps—most notably improving BERT pretraining/fine-tuning pipelines in NVIDIA’s widely used DeepLearningExamples repo. At NVIDIA he’s driven training and inference performance improvements, added stop-and-resume and single-GPU support, and optimized inference latency for enterprise deployments. Earlier roles span academic research on stylistic neural language generation, internships applying sequence labeling and time-series models, and a string of freelance projects that sharpened practical systems delivery. Based in California, he pairs rigorous academic foundations (3.85 GPA) with a pragmatic focus on reproducible, high-performance ML systems.
11 years of coding experience
4 years of employment as a software developer
University of California Santa CruzBE, Computer Science and Engineering, 8.77/10, BE, Computer Science and Engineering, 8.77/10 at SJCE
English, Kannada, Hindi, Telugu
Github Skills (17)
pytorch10
data-pipelines10
python10
machine-learning10
deep-learning10
bert10
nlp10
data-pipeline10
continuous-deployment9
ml-deployment9
performance-optimization9
automation9
automations9
azure-devops8
git8
Programming languages (3)
Github contributions (5)
NVIDIA/DeepLearningExamples
Mar 2019 - Apr 2021
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
Role in this project:
MLOps Engineer
MLOps EngineerContributions:4 reviews, 43 commits, 44 PRs in 2 years 1 month
Contributions summary:Sharath primarily focused on maintaining and improving the BERT pretraining and fine-tuning pipelines within the repository. They addressed data preparation issues, corrected performance calculations, and fixed single GPU support. The user also implemented features like stop-and-resume functionality and optimized inference latency. Their contributions suggest an emphasis on streamlining model training, deployment, and overall performance.
forecastingcaffe2translationspeech-recognitionstate-of-the-art
ProjectsUCSC/E2E-NLG-Personage
Nov 2017 - Mar 2018
Contributions:15 commits, 10 pushes, 1 branch in 3 months
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