Freelancer profile translated to English.
Back to original languageAbout Nathan
AI / Machine Learning Engineer specializing in PyTorch, Graph Neural Networks, optimization, and scientific machine learning, I help companies design robust machine learning models for technical, scientific, and industrial problems.
My expertise covers the development of complete ML pipelines: data analysis, prototyping, model training, evaluation, performance optimization, and progressive industrialization. I have strong experience with deep learning models applied to complex systems, particularly graph neural networks, simulation surrogate models, optimization, and microelectronics-related issues.
I can be involved in missions such as:
- Development of PyTorch / deep learning models;
- Creation of ML prototypes from complex data;
- Improvement or redesign of training pipelines;
- CPU/GPU performance optimization;
- Implementation of metrics, validation, and experiment tracking;
- Implementation of research papers or advanced methods;
- Technical support for R&D-intensive AI projects.
My goal is to transform complex problems into concrete, testable, and usable solutions, with a particular focus on code quality, experimental rigor, and business understanding.
French
Native or bilingual
English
Fluent
Remote only
Primarily works remotely
Experience
- Phd studentPhd StudentTECHMarch 2023 - Today (3 years and 3 months)Grenoble, France**Engineer and PhD Student**, I combine academic state-of-the-art with industrial production requirements. I design AI systems capable of processing complex, large-scale data.**AI Industrialization**: I don't just create models; I build the software infrastructure that allows them to be reliably trained, validated, and monitored.**Mastery of "Big Compute"**: Expert in distributed environments, I know how to run heavy models on HPC infrastructures.**Code Quality & Rigor**: I deliver structured, documented, and tested Python code, ready to be integrated into demanding professional environments.Key Technologies: PyTorch (Expert), GNN, HPC, Scientific Python.
- StmicroelectronicsDeep Learning EngineerTECHAugust 2022 - March 2023 (7 months)Crolles, France
Deep Learning R&D Engineer (Pre-Doctorate) STMicroelectronics
Technical and operational preparation for a large-scale industrial research project.ML Infrastructure Setup: Configuration of development environments and structuring of code to ensure experiment reproducibility.Rapid Prototyping: Development of initial Proofs of Concept (POCs) in Deep Learning to validate the feasibility of approaches on industrial data.PyTorch Expertise Development: Advanced mastery of the framework and implementation of Python development best practices.
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Education
- Generalist EngineerENSMM (École Nationale Supérieure de Mécanique et des Microtechniques)2022Cursus ingénieur axé sur l'intégration hardware/software. Spécialisation académique : Conception et Réalisation d'Objets Connectés (IoT, Systèmes embarqués). Projet de fin d'études mêlant computer vision et objets connectés pour l'industrie.