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AI Software Engineer | PhD
AI, Data & Machine learning expert specialized in industrial applications, with experience in both academic research and industry. Research goals: making deep learning models more robust (e.g. domain adaptation), interpretable (e.g. XAI) and data-efficient (unsupervised and weakly-supervised learning). Application areas: Computer vision, signal processing, Predictive maintenance, Prognostics & health management (PHM), Visual inspection
Université Sorbonne Paris Nord
Ph. D., Computer Science
January 1, 2018 – January 1, 2021
Technische Universität Berlin
Aeronautical and Astronautical Engineering
January 1, 2015 – January 1, 2016
ISAE-SUPAERO
Master of Engineering, Space Engineering, Data Science
January 1, 2013 – January 1, 2017
Lycée Janson-de-Sailly
Preparatory school in theoretical Mathematics, Physics and Computer Science
January 1, 2011 – January 1, 2013
Lycée Marie Laurencin
Baccalauréat S, mathematics
January 1, 2008 – January 1, 2011
Nestlé
Specialist Data Science & AI Engineering
May 1, 2026 – Present
Lausanne, Vaud, Switzerland
Ecorobotix
AI Software Engineer
June 1, 2025 – May 1, 2026
Yverdon, Vaud, Switzerland
EPFL
Scientist
September 1, 2022 – June 1, 2025
Lausanne, Vaud, Switzerland
EPFL
Scientist
April 1, 2021 – August 1, 2022
Lausanne, Vaud, Switzerland
Nagi Bioscience
Data Scientist & Software Engineer
April 1, 2021 – September 1, 2022
Lausanne, Vaud, Switzerland
ISAE-SUPAERO
Temporary teacher
January 1, 2019 – January 1, 2019
Greater Toulouse Metropolitan Area
Université Paris 13
Temporary teacher
January 1, 2018 – January 1, 2020
Villetaneuse, France
Safran Aircraft Engines
Data Scientist
January 1, 2018 – March 1, 2021
Moissy-Cramayel, Île-de-France, France
Airbus
Artificial Intelligence R&D Intern
April 1, 2017 – October 1, 2017
Greater Toulouse Metropolitan Area
CNES
Attitude and Orbit Control System Intern
March 1, 2016 – August 1, 2016
Greater Toulouse Metropolitan Area
Superprof
Private teacher in physics
January 1, 2016 – January 1, 2017
IRAP - Research lab in Astrophysics and Planetology
Intern
February 1, 2015 – June 1, 2015
Greater Toulouse Metropolitan Area
ONERA
Intern
July 1, 2014 – July 1, 2014
Greater Toulouse Metropolitan Area
Détection d'anomalies dans des données d'essai
January 1, 2016 – Present
Projet ingénierie de 3ème année en collaboration avec l'industrie. Étude et développement d'une solution de détection automatique d'anomalies dans des données d'essais de systèmes d'air aéronautiques. - Analyse de données - Machine Learning - Gestion de projet (chef de projet)
Spark - Level 1
IBM
June 24, 2026 – Present
Big Data Foundations - Level 1
IBM
June 24, 2026 – Present
Introduction to R
DataCamp
June 24, 2026 – Present
Deep Learning
Udacity
June 24, 2026 – Present
Hadoop Foundations - Level 1
IBM
June 24, 2026 – Present
Functional Program Design in Scala
Coursera
June 24, 2026 – Present
Functional Programming Principles in Scala
Coursera
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Python for Data Science
DataCamp
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse experience across research institutions (EPFL, Université Sorbonne Paris Nord, ISAE-SUPAERO) and various industries (aerospace, agriculture, biotech, consumer goods) demonstrates adaptability and a broad interest in applying ML across different domains. The involvement in teaching and mentoring suggests a collaborative and knowledge-sharing mindset. The transition from academic research to industry roles (Ecorobotix, Nestlé) indicates a practical, results-oriented approach. The project 'Détection d'anomalies' shows initiative and project management skills. The target role of ML Engineer aligns well with the candidate's deep learning, computer vision, and MLOps experience.
Soft Skills & Operational Fit
The candidate's experience as a Scientist at EPFL, including mentoring students, teaching, and presenting at conferences, indicates strong communication and collaboration skills. Project leadership experience (Détection d'anomalies) and involvement in a multi-disciplinary startup (Nagi Bioscience) suggest adaptability and problem-solving capabilities. The teaching roles also highlight an ability to explain complex technical concepts.