Karan Shukla
Software Engineer at Google
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolKaran Shukla is a software engineer with a decade of experience building AI tooling, transparency, and infrastructure for top tech companies across San Francisco. He has led Model Transparency Tooling at Google and drove system-level AI transparency and provenance efforts at Meta, shipping AI System Cards across 20+ product surfaces and prototyping C2PA provenance ingestion that influenced company-wide commitments. Comfortable across Python and JavaScript, Karan contributes to open-source ML interpretability work such as TensorFlow's TCAV, where he added proto-formatted outputs and Relative TCAV support. Currently focused on Chrome AI memory, context, and personalization, he pairs technical leadership with cross-functional execution—bridging product, UX, internationalization, and policy to operationalize responsible AI.
11 years of coding experience
4 years of employment as a software developer
Bachelor of Science (B.S.) Computer Science, Bachelor of Science (B.S.) Computer Science at The University of Texas at Dallas
English
Stackoverflow
1,023reputation
40kreached
5answers
36questions
Github Skills (12)
machine-learning10
interpretation10
tensorflow10
python10
protobuffer10
protobuf10
gensim6
docker6
scipy6
try-catch6
numpy6
unicode6
Programming languages (5)
Github contributions (5)
tensorflow/tcav
Jul 2019 - Aug 2019
Code for the TCAV ML interpretability project
Role in this project:
ML Engineer
ML EngineerContributions:15 commits, 9 PRs, 11 comments in 1 month
Contributions summary:Karan implemented features related to TCAV (Testing with Concept Activation Vectors) interpretability project. They modified the codebase to return TCAV results in a proto format, adding a new `return_proto` argument to the `TCAV.run()` method and modifying several files, including `utils.py`, `tcav.py`, and `results.proto`, and added the `target_class` int. In addition, they updated the `results.proto` to include CAVaccuracies. Furthermore, they added support for Relative TCAV.
machine-learninginterpretabilitytcav
tensorflow/model-card-toolkit
Jul 2020 - Apr 2022
A toolkit that streamlines and automates the generation of model cards
Contributions:5 releases, 1 review, 168 commits in 1 year 9 months
streamlinespythonmodel-cardsdeep-learningautomates
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