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Timothé WatteauTW

Timothé Watteau

Algorithm Engineer

EUR 450/Tag
Lausanne, CH
0-2 Jahre

Durchschnittliche Reaktionszeit: 1h

Über Timothé

I'm an Algorithm Engineer with 1 year of experience, primarily passionate about creating user-friendly systems for complex problems using Machine Learning. I also have a great interest in software engineering and building clean, efficient systems.
  • Französisch

    Muttersprachlich oder zweisprachig

  • Englisch

    Muttersprachlich oder zweisprachig

  • Deutsch

    Grundkenntnisse

Nur remote
Führt Projekte hauptsächlich remote aus

Projekt- und Berufserfahrung

  • Freshape
    Algorithm Engineer
    August 2025 - Heute (10 Monate)
    Lausanne, Switzerland
    • Worked on an image enhancement pipeline for embedded cameras outputs from miniature systems.
    • Developed a multimodal digital tutor prototype leveraging LLMs to assist children with homework, merging voice interaction and paper capture for personalized learning.
    Computer Vision Pytorch
  • Freshape
    Algorithm Engineer Intern
    Februar 2025 - Juli 2025 (5 Monate)
    Lausanne, Schweiz
    • Designed and delivered an image-to-vector solution turning hand-drawn sketches into professional-quality visuals, enabling users to accelerate their workflow by converting complex ideas into clear, polished illustrations.
    • Implemented GPT-Image-1 integration via the OpenAI API to power state-of-the-art image-based sketch enhancement.
    • Implemented a robust CI/CD pipeline using GitLab and Docker, for reliable, scalable, automated software deployment.
    • Engineered a custom image vectorization algorithm with fine-tuned OCR on user data and advanced color clustering, streamlining content editing and improving accessibility for end users.
    CI/CD-Management Computer Vision Entwurfsmuster Frontend Interface-Entwicklung Pytorch
  • École de Technologie Supérieure
    Machine Learning Research Intern
    September 2023 - Februar 2024 (5 Monate)
    Montreal, QC, Canada
    • Designed a cutting-edge methodology for optimizing airport ground movements through Multi-Agent Reinforcement
    Learning, achieving a 5% reduction in airport taxiing emissions, utilizing PPO with custom PyTorch models (TreeLSTM).
    • Built a scalable MARL environment for optimizing speed and trajectories in real-world airport scenarios simulation.
    Pytorch Entwurfsmuster

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Ausbildung und Abschlüsse

  • Master of Science - Computer Science
    Université de Technologie de Belfort-Montbéliard
    2025
    Relevant coursework: Machine Learning, Deep Graph Learning, Data Science, Big Data & BI, Google Cloud Platform.
  • Master's Degree exchange
    Politecnico di Milano
    2025
    Coursework: Deep Learning, Game Theory, Quantitative Finance

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