Bharathan Balaji
Senior Applied Scientist at Amazon
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolBharathan Balaji is a Senior Applied Scientist at Amazon leveraging a PhD in Computer Engineering from UC San Diego and a decade of experience to apply machine learning to sustainability, smart buildings, and IoT problems. He helped launch and grow AWS offerings like SageMaker RL and DeepRacer, and contributes practical SageMaker example notebooks that help customers implement RL and hyperparameter tuning workflows. With 62 publications and over 3,000 citations, his work bridges rigorous academic research and production ML systems that address waste reduction, education, and automation. Based in Seattle, he combines deep algorithmic expertise with hands-on engineering to move research into customer-facing tools. An interesting throughline in his career is translating complex reinforcement learning research into accessible demos and developer-facing examples that accelerate adoption.
10 years of coding experience
Masters, Electrical Engineering, Masters, Electrical Engineering at University of California, San DiegoUniversity of California San DiegoBachelor of Technology, Electronics and Communication Engineering, Bachelor of Technology, Electronics and Communication Engineering at Visvesvaraya National Institute of TechnologyHigh School Certificate, Computer Science, High School Certificate, Computer Science at Atomic Energy Central School
Hindi, Tamil, English
Github Skills (12)
jupyter-notebook10
machine-learning10
xgboost10
aws10
sagemaker10
data-science9
deep-learning8
deeplearning-ai8
trainings7
reinforcement-learning7
python7
inference7
Programming languages (5)
Github contributions (5)
aws/amazon-sagemaker-examples
Apr 2019 - Nov 2019
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
Role in this project:
ML Engineer
ML EngineerContributions:13 commits, 18 PRs, 1 branch in 7 months
Contributions summary:Bharathan primarily contributes to example notebooks for Amazon SageMaker, showcasing machine learning model development, training, and deployment. They have made changes related to hyperparameter tuning using XGBoost, and also made several revisions to the example notebooks, including fixes and corrections. The user's work is focused on demonstrating various machine learning techniques and providing practical examples within the SageMaker ecosystem.
pythonjupyter-notebooktrainingawssagemaker
amazon-science/carbon-assessment-with-ml
Feb 2023 - Mar 2025
CaML: Carbon Footprinting of Household Products with Zero-Shot Semantic Text Similarity
Contributions:12 PRs, 25 pushes, 5 comments in 2 years 1 month
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