Ramanish Singh
Machine Learning Engineer at Meta
Menlo Park, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRamanish Singh is a Machine Learning Engineer with six years of cross-disciplinary experience applying deep learning and scientific modeling to industrial problems, now based in Menlo Park and working at Meta. He holds a PhD in Chemical Engineering and a master’s in Data Science from the University of Minnesota, blending rigorous domain knowledge with practical ML skills—particularly in PyTorch. His internships at Dow and Applied Materials produced production-relevant innovations: a CNN-based defect detector for recycled PE films that reached >95% accuracy with massive experimental speedups, and a reduced-order plasma chemistry Python package that improved simulation efficiency. Ramanish began his career as a process engineer, giving him uncommon strengths in bridging laboratory experiments, simulation, and scalable ML deployments. Colleagues can expect a researcher-engineer who converts scarce experimental data into robust training pipelines and reproducible code for production environments.
6 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Chemical Engineering, Doctor of Philosophy - PhD, Chemical Engineering at University of MinnesotaIndian Institute of Technology Kanpur
English, Hindi
Github Skills (38)
python10
machine-learning10
data-loading10
pytorch9
gpu9
neural-network9
deep-learning9
gpu-acceleration8
tensorflow8
acceleration8
python-interface8
numpy8
component-based8
data-collection8
hierarchical8
Programming languages (4)
Github contributions (5)
mosdef-hub/reproducibility_study
Aug 2021 - Dec 2022
Repo for data collection, discussion, etc for a MoSDeF reproducibility study.
Contributions:42 reviews, 257 commits, 66 PRs in 1 year 4 months
reproducibilitydata-collectionmosdef
ramanishsingh/reproducibility_study
Aug 2021 - Dec 2024
Repo for data collection, discussion, etc for a MoSDeF reproducibility study.
Contributions:6 PRs, 212 pushes, 17 branches in 3 years 4 months
reproducibilitydata-collectionmosdef
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