Melih Elibol
Senior Research Scientist at NVIDIA
California, United States
Summary
🤩
Rockstar🎓
Top SchoolMelih Elibol is a Senior Research Scientist at NVIDIA with 11 years of experience specializing in AI/ML and distributed systems. He holds a PhD from UC Berkeley and a liberal arts degree from Harvard, blending deep research rigor with broad intellectual curiosity. At NVIDIA he focuses on making large-scale ML systems reliable and performant, and his open-source contributions to Ray demonstrate hands-on skills in back-end engineering, DevOps, build automation, and debugging for a widely used AI compute engine. Colleagues describe him as a pragmatic problem-solver who simplifies complex code paths and improves observability and object transfer in production pipelines. Based in California, he combines academic depth with practical systems engineering to push ML infrastructure from prototype to production.
10 years of coding experience
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at University of California, BerkeleyBachelor of Liberal Arts, Bachelor of Liberal Arts at Harvard University
English, Turkish
Stackoverflow
86reputation
7kreached
9answers
0questions
Github Skills (18)
python10
distributed-systems10
tensorflow10
ray10
data-science9
parallel9
machine-learning9
devops9
deep-learning9
optimization8
deploying8
multiprocessing6
logistic-regression6
api6
pool6
Programming languages (3)
Github contributions (5)
ray-project/ray
Nov 2017 - Aug 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 & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:27 commits, 52 PRs, 83 pushes in 8 months
Contributions summary:Melih primarily contributed to the implementation of a synchronous parameter server example, which is part of the larger Ray AI compute engine project. Their work involved adding and refining code examples, including documentation updates, and minor simplifications of the code base. Furthermore, they fixed bugs in the build process related to third-party installations and made several improvements regarding logging and object transfer, indicating a focus on performance, debugging, and build automation.
pythonconsistsruntimetensorflowserving
elibol/ray
Nov 2017 - Nov 2018
A high-performance distributed execution engine
Contributions:2 PRs, 455 pushes, 74 branches in 1 year
rayexecution-engineperformancedistributed-computingdistributed-systems
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.