Divyang Doshi
Machine Learning Engineer at Apple
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolDivyang Doshi is a Machine Learning Engineer with nine years of experience specializing in LLM inference optimization and productionizing deep learning systems. Currently at Apple after roles at Pienso and NC State, he has shipped multimodal search and fine-tuned CLIP for a high‑accuracy visual search vertical, built high‑performance inference clients in Go, and deployed models on AWS EKS. His research-to-production track record includes BERT-based solutions for peer review automation and semi-supervised pipelines, a 92% helpfulness classifier, and practical migrations and CI integrations for legacy Rails platforms. Comfortable across the stack, he has contributed full‑stack fixes to the NSF-supported Expertiza project and brings a habit of squeezing runtime and deployment bottlenecks—turning research prototypes into scalable, observable services.
9 years of coding experience
4 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at North Carolina State UniversityBachelor of Engineering Computer Science, Bachelor of Engineering Computer Science at Birla Institute of Technology, Mesra
Gujarati, Hindi, English
Github Skills (12)
ruby10
ruby-rails10
peer10
rails10
st10
peer-to-peer10
ed2551910
rspec9
git9
javascript8
docker4
dockers4
Programming languages (2)
Github contributions (5)
expertiza/expertiza
Jan 2022 - Jan 2023
Expertiza is a web application through which students can submit and peer-review learning objects (articles, code, web sites, etc). The Expertiza project is supported by the National Science Foundation.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:2 reviews, 53 commits, 16 PRs in 1 year
Contributions summary:Divyang primarily contributed to bug fixes and minor enhancements across various parts of the Expertiza web application. They addressed issues related to student team creation routes, assignment editing, and score view functionality. Their work involved modifications to Ruby on Rails code, Javascript/React components, and configuration files related to deployment, demonstrating a broad understanding of the application's codebase. The user also touched upon the front-end and back-end aspects of the application.
nationalpeer-reviewexpertiza-wikidjangoexpertiza
divyang02/PrincetonAlgorithms
Jan 2017 - Jun 2017
Contributions:9 commits, 8 pushes, 1 branch in 5 months
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