Noémien Kocher - Software Engineer at Taurus SA

Noémien Kocher

Software Engineer at Taurus SA

Crissier, Vaud, Switzerland

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Summary

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Senior

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Top School

Noémien Kocher is a full-stack software engineer with 12 years of experience, currently building secure, scalable systems at Taurus SA from his base in Crissier, Switzerland. He mainly programs in Go and TypeScript and has a strong research-to-production background from EPFL, where he implemented privacy-preserving, zero‑trust and decentralized systems and operated them with container orchestration and infrastructure-as-code. His open-source contributions include enhancing the Go crypto library dedis/kyber—adding distributed key generation examples and a Neff shuffle implementation—and ML work on ENAS in PyTorch, reflecting fluency across cryptography, distributed systems and ML. Comfortable across the full lifecycle (architecture, CI/CD, monitoring) and with hands-on mentoring experience, he blends academic rigor (MSc with a top-tier conference publication) with practical deployment skills that help turn advanced protocols into operational services.

12 years of coding experience

4 years of employment as a software developer

Master of Science (M.Sc), Software engineering, Master thesis published at a top-tier conference in Hong-Kong, Master of Science (M.Sc), Software engineering, Master thesis published at a top-tier conference in Hong-Kong at HES-SO Haute école spécialisée de Suisse occidentale

Bachelor of Science (B.Sc), Computer Science, informatique, software engineering, Received the alumni association price for my bachelor project., Bachelor of Science (B.Sc), Computer Science, informatique, software engineering, Received the alumni association price for my bachelor project. at Ecole d'ingénieurs et d'architectes de Fribourg / Hochschule für Technik und Architektur Freiburg

Special Math Courses (Cours de math spécifique - CMS), Maths, Physics, Biology, Chemistry, Computer science, Special Math Courses (Cours de math spécifique - CMS), Maths, Physics, Biology, Chemistry, Computer science at Ecole polytechnique fédérale de Lausanne

Certificat Fédéral de Capacité (CFC), Maturité professionnelle technique (MPT), Informatique, Projet de CFC nominé à la 3e place au niveau national, Certificat Fédéral de Capacité (CFC), Maturité professionnelle technique (MPT), Informatique, Projet de CFC nominé à la 3e place au niveau national at Ecole des métiers de Fribourg

English, French, German

Stackoverflow

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1,354reputation

86kreached

13answers

2questions

Github Skills (21)

pytorch10

python10

go10

trainings10

cryptography10

modeling10

neural-architecture-search10

elliptic-curve9

crypto9

machine-learning9

crypt9

json7

ruby-on-rails6

amazon-s36

actionmailer6

Programming languages (17)

C#JavaCSSSchemeVueGoHTMLJupyter Notebook

Github contributions (5)

dedis/kyber

Jan 2020 - Nov 2022

Advanced crypto library for the Go language

Role in this project:

Back-end Developer

Contributions:3 releases, 17 reviews, 38 commits in 2 years 10 months

Contributions summary:Noémien contributed to the advanced crypto library for Go language by implementing and updating examples for the dkg (distributed key generation) process. They added scenarios showcasing how to generate public and private keys, re-encryption methods, and partial decryptions using the Pedersen DKG scheme. Furthermore, the user incorporated the Neff shuffle protocol for shuffling sequences of ElGamal pairs, including the necessary proof generation and verification steps. These changes indicate a focus on improving the library's cryptographic functionality.

cryptogo-languagecrypto-libraryelliptic-curvesgo

carpedm20/ENAS-pytorch

Oct 2018 - Nov 2018

PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

Role in this project:

ML Engineer

Contributions:10 commits, 2 PRs, 4 comments in 26 days

Contributions summary:Noémien primarily contributed to the core functionality of the ENAS-pytorch project, focusing on training and architecture search. They added features to handle single-dag training and loading, including the ability to load a predefined architecture. They modified the trainer class to support different training modes, including single dag training and updated utilities for saving and loading DAGs. This involved changes to the training loop and the evaluation process.

neural-architecture-searchpytorchgoogle-brain

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