Neelesh Dodda
Software Development Engineer III - AWS Clean Rooms at Amazon Web Services (AWS)
San Jose, California, United States
Summary
👤
Senior🎓
Top SchoolNeelesh Dodda is a software engineer and entrepreneur with nine years of experience building privacy-preserving ML and cloud-native systems, currently working as SDE III on AWS Clean Rooms. A UC Berkeley EECS graduate and cofounder/CTO of DiscreetAI, he blends startup product instincts with deep hands-on engineering—shipping features for lookalike audience modeling, custom modeling, and differential privacy in AWS Clean Rooms ML. His background includes building ML debugging tooling in SageMaker and automating EC2 host lifecycle services, demonstrating a pattern of turning complex infrastructure and privacy requirements into production-ready services. Based in San Jose, he contributes to AWS SageMaker projects on GitHub and is known for bridging research-grade privacy concepts with pragmatic engineering at scale.
9 years of coding experience
6 years of employment as a software developer
Bachelor of Science (B.S.), Electrical Engineering and Computer Science (EECS), Bachelor of Science (B.S.), Electrical Engineering and Computer Science (EECS) at University of California, BerkeleyEvergreen Valley High School
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (27)
full-stack9
python8
machine-learning8
data-science7
federated-learning7
backend7
jupyter-notebook7
frontend6
jupyterlab6
javascript6
flask5
data-visualization5
django5
notebook5
aggregator4
Programming languages (5)
Github contributions (5)
DiscreetAI/dml-library-js
Apr 2019 - Nov 2019
Interoperable and decentralized machine learning in JavaScript.
Contributions:25 commits, 2 PRs, 13 pushes in 7 months
interoperablejavascriptmachine-learningdecentralized-machine-learningdecentralized
DiscreetAI/decentralized-ml
Mar 2018 - Nov 2020
Full stack service enabling decentralized machine learning on private data
Contributions:711 commits, 130 PRs, 292 pushes in 2 years 8 months
pythondata-sciencefull-stackmachine-learningdecentralized-machine-learning
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