Rishi Kulkarni
Senior Director, Machine Learning Engineering at IntelyCare
Boston, Massachusetts, United States
Summary
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Senior🎓
Top SchoolRishi Kulkarni is a Senior Director of Machine Learning Engineering with nine years of experience applying Bayesian statistics and computational methods to healthcare, biotech, and pharma. He blends deep academic training (PhD in Chemistry from UC Berkeley and postdoctoral work at Stanford) with hands-on product delivery, shepherding models from prototyping to stakeholder adoption and production deployment. Rishi has a track record of building robust statistical tooling—contributing to high-profile open-source projects like Numba by implementing and testing NumPy random routines—bringing scientific rigor to engineering workflows. He excels at interdisciplinary problems spanning nonparametric estimation to computational chemistry and biology, and regularly communicates technical work at national and international conferences. Based in Boston, he pairs leadership of cross-functional teams with a coder’s mentality, often engaging directly in back-end implementation and testing. Outside core ML work, he’s known for clear communication and community engagement—serving as a National Science Bowl quizmaster on GitHub profile—underscoring his knack for education and outreach.
8 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy (PhD) Chemistry, Doctor of Philosophy (PhD) Chemistry at University of California, BerkeleyMira Loma High SchoolBachelor of Arts (B.A.) Biochemistry and Molecular Biology, Bachelor of Arts (B.A.) Biochemistry and Molecular Biology at Boston University
Github Skills (8)
compiler10
compiler-compiler10
numba10
python10
numpy10
testing9
llvm7
cuda5
Programming languages (7)
Github contributions (5)
numba/numba
May 2021 - Feb 2022
NumPy aware dynamic Python compiler using LLVM
Role in this project:
Back-end Developer
Back-end DeveloperContributions:21 reviews, 33 commits, 6 PRs in 9 months
Contributions summary:Rishi primarily contributed to implementing and testing the `np.random.dirichlet` and `np.random.noncentral_chisquare` functions within the `numba` project. Their work involved creating overloads for these NumPy random number generator functions, incorporating various size arguments, and ensuring correct behavior with different input parameters. The user also added tests to validate these implementations and addressed code style issues to maintain code quality.
cudapythonparallelnumpynumba
rishi-kulkarni/SpykeMapper
Jun 2017 - Jul 2021
Contributions:24 pushes, 2 branches in 4 years 1 month
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