Shantanu Chhabra
Engineering Manager, Machine Learning at Apple
Cupertino, California, United States
Summary
🤩
Rockstar🎓
Top SchoolShantanu Chhabra is an Engineering Manager in Machine Learning at Apple with a decade of experience building production ML systems and shipping widely used accessibility and computer-vision features to billions of devices. He has a track record of moving ideas from concept to product—architecting multiple platform products, co-founding a new team, and mentoring 10+ engineers to amplify impact across organizations. His work spans from low-level backend engineering (contributions to Apple’s open-source Turi Create, improving type conversions and plot APIs) to high-profile consumer features like Door Detection, Visual Look Up, and VoiceOver enhancements. Comfortable leading cross-functional teams, he blends rigorous CS training from Carnegie Mellon with hands-on experience in feature engineering and model deployment at scale. An understated strength is his ability to translate research and prototypes into robust developer APIs and production-ready tooling that enable other teams to build novel ML use cases.
11 years of coding experience
7 years of employment as a software developer
Bachelor of Science (B.S.) Computer Science Robotics, Bachelor of Science (B.S.) Computer Science Robotics at Carnegie Mellon UniversityCBSE Grade 12 English Computer Science Mathematics Phyics Chemistry, CBSE Grade 12 English Computer Science Mathematics Phyics Chemistry at Delhi Public School - R. K. Puram
English, Hindi
Github Skills (6)
unit-testing10
machine-learning10
python10
datatypes10
error-handling9
deep-learning9
Programming languages (2)
Github contributions (5)
apple/turicreate
Mar 2018 - Jan 2020
Turi Create simplifies the development of custom machine learning models.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:4 releases, 8 reviews, 137 commits in 1 year 10 months
Contributions summary:Shantanu primarily worked on improving the codebase's handling of data type conversions, focusing on integer and float conversions from strings, and implementing error messages. They also added unit tests to ensure the robustness of these type conversions. Furthermore, the user contributed to the Plot API by adding code for the independent plots, which suggests involvement in the library's data visualization capabilities.
machine-learning-modelsmachine-learningdeep-learningpython
shantanuchhabra/turicreate
Mar 2018 - May 2020
Turi Create simplifies the development of custom machine learning models.
Contributions:428 pushes, 201 branches, 1 tag in 2 years 2 months
machine-learning-models
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