Kunal Gosar
Head Of AI Platform
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolKunal Gosar is a technology leader with a decade of experience building AI and distributed systems, currently serving as Head of AI & OCTO and Director of Engineering at Addepar. He founded Arcus, an agentic workflows startup acquired by Addepar, and has led engineering teams delivering AI products that leverage nearly $9T of platform asset data. His background spans research at Berkeley’s RISE Lab, engineering at Google Cloud, and scaling platform infrastructure at Instabase, blending deep ML and systems chops with product and go-to-market thinking. An active open-source contributor, he has improved core data-frame functionality in Ray and helped scale Pandas workflows via Modin, reflecting hands-on work across back-end, data engineering, and CI/CD. Known for growing teams and shipping production-grade AI systems, he combines technical depth with founder-level execution.
10 years of coding experience
8 years of employment as a software developer
Bachelor’s Degree B.S. Electrical Engineering and Computer Science (EECS), Bachelor’s Degree B.S. Electrical Engineering and Computer Science (EECS) at University of California, BerkeleyInternational Baccalaureate , International Baccalaureate at International School of Basel, Switzerland
Hindi, German, Kutchi, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (23)
benchmark10
javascript10
python10
pandas10
benchmarking10
user-interface10
travis-ci10
data-structure10
cicd10
distributed-computing10
data-structures10
qml10
ray10
data-science9
testing9
Programming languages (8)
Github contributions (5)
ray-project/ray
Feb 2018 - Jun 2018
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
Back-end Developer / Data Scientist
Back-end Developer / Data ScientistContributions:19 commits, 22 PRs, 162 comments in 3 months
Contributions summary:Kunal primarily contributed to the implementation of core functionalities within the Ray DataFrame library. They added implementations for methods like `equals`, `query`, and various operators, along with the supporting tests. The user also worked on refactoring the internal implementation of the DataFrame, by moving the index functions to remote, improving the efficiency of the methods such as iterators and implementing methods like `mode`, `to_datetime`, and `get_dummies`. These contributions enhanced the DataFrame's capabilities and performance within the Ray ecosystem.
pythonconsistsruntimetensorflowserving
modin-project/modin
Jun 2018 - Jul 2018
Modin: Scale your Pandas workflows by changing a single line of code
Role in this project:DevOps Engineer & Data Engineer
DevOps Engineer & Data EngineerContributions:1 release, 11 commits, 23 PRs in 17 days
Contributions summary:Kunal's contributions primarily center around setting up and configuring the continuous integration and continuous deployment (CI/CD) environment using Travis CI. They established the necessary infrastructure for running tests and building the project, including the installation of dependencies and specifying Python versions. Furthermore, the user demonstrated data engineering skills by contributing to the benchmarking infrastructure, creating benchmarks, and making the project ready for performance comparisons.
analyticspythonline-of-codedata-sciencedataframe
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