Enrico Fini
Member Of Technical Staff at Microsoft AI
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolEnrico Fini is a research-focused machine learning engineer with nine years of experience building and shipping self-supervised and applied AI systems across top research labs and industry teams. He earned a PhD in Artificial Intelligence from the University of Trento and has held research and internship roles at Apple, Meta FAIR, Amazon, SAP AI Research, Inria, and ESA, now continuing as Member of Technical Staff at Microsoft AI. Enrico contributes to open-source ML tooling (notably enhancements to the popular solo-learn library), where he improved model implementations, fixed memory leaks, and expanded contrastive and clustering methods. He blends rigorous academic training with hands-on production fixes and reproducible training pipelines, often tackling subtle engineering issues like inplace operations and CUDA instructions that improve robustness. Based in Zurich, he brings a practical researcher’s mindset to bridging novel algorithms and reliable engineering.
9 years of coding experience
3 years of employment as a software developer
Exchange Program, Computer Science, Exchange Program, Computer Science at University of AdelaideLaurea triennale, Ingegneria Informatica, Elettronica e delle Telecomunicazioni, 109, Laurea triennale, Ingegneria Informatica, Elettronica e delle Telecomunicazioni, 109 at Università degli Studi di ParmaDoctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at University of Trento80, 80 at Liceo ScientificoMaster of Science (MS), Computer Science and Engineering, 110, Master of Science (MS), Computer Science and Engineering, 110 at Politecnico di Milano
Italian, English, French, Spanish
Github Skills (6)
pytorch10
machine-learning10
self-supervised-learning10
contrastive-learning10
python9
backend7
Programming languages (6)
Github contributions (5)
vturrisi/solo-learn
May 2021 - Jul 2022
solo-learn: a library of self-supervised methods for visual representation learning powered by Pytorch Lightning
Role in this project:
ML Engineer
ML EngineerContributions:61 reviews, 66 commits, 92 PRs in 1 year 2 months
Contributions summary:Enrico contributed to the self-supervised learning library by improving existing models and adding new ones. The contributions included removing inplace operations, providing instructions for CUDA, and improving the consistency of contrastive dataloaders. Additionally, the user added MoCo V2+, SwAV, and DeepCluster V2 models, along with corresponding bash scripts for training. The user also addressed memory leaks and fixed bugs, enhancing the library's functionality and usability.
nvidia-dalivisual-representationrepresentation-learningself-supervised-learningpytorch-lightning
DonkeyShot21/batch-level-distillation
Jul 2020 - Jan 2021
Contributions:4 commits, 3 pushes, 1 branch in 6 months
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.