Sanyam Kapoor
Research Scientist at NVIDIA
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolSanyam Kapoor is a research scientist based in New York with 11 years of experience building scalable ML and engineering systems across industry leaders including NVIDIA, Netflix, AWS, Uber, and Google. He blends deep academic training (PhD and MS from NYU) with hands-on engineering, contributing to production-ready data pipelines and semantic code search tooling in prominent open-source projects such as kubeflow/examples. His work spans applied research and DevOps — from Apache Beam data ingestion and Dataflow resiliency to model-focused internships and AI residency roles — enabling reliable ML workflows at scale. As a former founder and early-stage engineer, he brings product intuition and operational pragmatism to research problems. Notably, he has implemented robust preprocessing and worker-configuration features that make large-scale code-search pipelines more fault-tolerant and deployable. He delivers work at the intersection of research and production, turning novel ideas into maintainable, production-grade systems.
11 years of coding experience
5 years of employment as a software developer
Bachelor of Technology - BTech Computer Science, Bachelor of Technology - BTech Computer Science at Indian Institute of Technology HyderabadDoctor of Philosophy - PhD Data Science, Doctor of Philosophy - PhD Data Science at New York University
Github Skills (13)
google-cloud-dataflow10
bigquery10
data-pipelines10
data-pipeline10
apache-beam10
python10
tensorflow9
docker8
cicd8
dockers8
ml7
kubeflow7
machine-learning7
Programming languages (20)
Github contributions (5)
kubeflow/examples
May 2018 - Aug 2018
A repository to host extended examples and tutorials
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:37 commits, 32 PRs, 119 comments in 2 months
Contributions summary:Sanyam contributed to the development of a semantic code search example, primarily focusing on data ingestion and pre-processing. They built a pipeline for code search preprocessing using Apache Beam, including data ingestion and tokenization of code and docstrings. The user also worked on setting up the environment by adding requirements for the project and creating the setup configurations. Additionally, the user implemented error handling within the Dataflow pipeline and added the ability to specify worker configurations for Dataflow execution.
kuberneteshostextended
activatedgeek/galileo
Sep 2024 - Apr 2025
Contributions:603 pushes, 6 branches, 2 issues in 6 months
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