Machine Learning Enginee

Club IA Legal is seeking a part-time Machine Learning Engineer to build and optimize machine learning models for legal data analysis and decision-support tools. The role includes developing scalable production algorithms, evaluating and documenting model performance, and collaborating with legal and technical stakeholders while supporting data security, code quality, and pipeline improvement.

Responsibilities:

• The Machine Learning Engineer will design, build, and optimize machine learning models to support legal data analysis and decision-support tools
• Day-to-day responsibilities include collecting and preprocessing data, experimenting with pattern recognition and neural network architectures, and implementing scalable algorithms in production environments
• The role involves collaborating with legal and technical stakeholders to translate requirements into technical solutions, evaluating model performance using statistical methods, and documenting processes and results
• The Machine Learning Engineer will also help maintain code quality, ensure data security and compliance, and contribute to continuous improvement of the machine learning pipeline

Requirements:

• Strong foundation in Computer Science and Algorithms, with the ability to design efficient, maintainable solutions
• Proficiency in Pattern Recognition and Neural Networks for building and refining predictive models
• Solid understanding of Statistics for data analysis, model evaluation, and performance optimization
• Experience with common machine learning frameworks and languages (e.g., Python, TensorFlow, PyTorch, scikit-learn)
• Familiarity with data preprocessing, feature engineering, and handling large or complex datasets
• Ability to communicate clearly with cross-functional teams
• Bachelor's degree or higher in Computer Science, Data Science, Mathematics, Engineering, or a related field, or equivalent practical experience
• Experience or interest in legal tech, compliance, or working with text-based and document-centric data is a plus