Machine Learning Engineer

Join Snap Inc. as a Machine Learning Engineer in the Brain-Computer Interface team. You will work on state-of-the-art machine learning for time series data, developing the next generation of wearable device sensing. This role is based in our Paris office and involves collaboration with hardware and software teams worldwide. Responsibilities include designing, implementing, and deploying ML models and systems, developing tools for training and evaluating ML algorithm performance, and writing clean, well-designed code.

Responsibilities

• Concevoir, mettre en œuvre et déployer des modèles et des systèmes d'apprentissage automatique, en particulier pour les données de séries temporelles.
• Collaborer avec les équipes de recherche et de développement pour déployer de nouveaux systèmes et algorithmes d'apprentissage automatique, et résoudre les problèmes d'intégration.
• Développer des outils pour l'entraînement, le test et l'évaluation des performances des algorithmes d'apprentissage automatique.

Requirements

• Strong knowledge in Python

Deep understanding of ML principles, algorithms and systems

• Ability to understand, debug and improve existing code as well as develop new algorithms using advanced time series and ML techniques
• Great spoken and written communication skills; ability to clearly explain complex ideas in a cross-functional team through design docs and presentations
• Strong knowledge in Python or C++

Fluency in English

• MSc in Machine Learning or Computer Science, or equivalent field
• Experience with engineering workflows including version control (git) and code reviews
• 3+ years of research or engineering experience with ML approaches
• Familiarity with AI agents to support engineering and ML workflows
• Expertise in algorithms for digital signal processing, time series, EEG or other physiological sensors data
• Experience with continuous integration and code quality tools

Preferred

• Experience with ML frameworks (e.g. PyTorch, mlflow), as well as with cloud environments (Google Cloud, AWS)

Experience with real-time ML and sensor systems

• PhD in Machine Learning or Computer Science, or equivalent field
• Experience developing AI agentic tools to support engineering workflows and ML experimentation